Section 1: Import Libraries¶
In [65]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sentence_transformers import SentenceTransformer
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score, davies_bouldin_score
import scipy.cluster.hierarchy as sch
from sklearn.cluster import AgglomerativeClustering
from sklearn.cluster import DBSCAN
from sklearn.neighbors import NearestNeighbors
from sklearn.mixture import GaussianMixture
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
try:
import umap
import hdbscan
from kmodes.kprototypes import KPrototypes
from sentence_transformers import SentenceTransformer
except ImportError:
print("Some advanced packages are not installed. You may need to install them with pip.")
Section 2: Data Loading and Initial Exploration¶
In [54]:
# Dataset
df_original = pd.read_csv('shopping_behavior_updated.csv')
df = df_original.copy() # Create a working copy of the original data
# First few rows
print(df.head())
Customer ID Age Gender Item Purchased Category Purchase Amount (USD) \
0 1 55 Male Blouse Clothing 53
1 2 19 Male Sweater Clothing 64
2 3 50 Male Jeans Clothing 73
3 4 21 Male Sandals Footwear 90
4 5 45 Male Blouse Clothing 49
Location Size Color Season Review Rating Subscription Status \
0 Kentucky L Gray Winter 3.1 Yes
1 Maine L Maroon Winter 3.1 Yes
2 Massachusetts S Maroon Spring 3.1 Yes
3 Rhode Island M Maroon Spring 3.5 Yes
4 Oregon M Turquoise Spring 2.7 Yes
Shipping Type Discount Applied Promo Code Used Previous Purchases \
0 Express Yes Yes 14
1 Express Yes Yes 2
2 Free Shipping Yes Yes 23
3 Next Day Air Yes Yes 49
4 Free Shipping Yes Yes 31
Payment Method Frequency of Purchases
0 Venmo Fortnightly
1 Cash Fortnightly
2 Credit Card Weekly
3 PayPal Weekly
4 PayPal Annually
In [55]:
# Basic information
print(df.info())
# Descriptive statistics
print(df.describe())
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3900 entries, 0 to 3899
Data columns (total 18 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Customer ID 3900 non-null int64
1 Age 3900 non-null int64
2 Gender 3900 non-null object
3 Item Purchased 3900 non-null object
4 Category 3900 non-null object
5 Purchase Amount (USD) 3900 non-null int64
6 Location 3900 non-null object
7 Size 3900 non-null object
8 Color 3900 non-null object
9 Season 3900 non-null object
10 Review Rating 3900 non-null float64
11 Subscription Status 3900 non-null object
12 Shipping Type 3900 non-null object
13 Discount Applied 3900 non-null object
14 Promo Code Used 3900 non-null object
15 Previous Purchases 3900 non-null int64
16 Payment Method 3900 non-null object
17 Frequency of Purchases 3900 non-null object
dtypes: float64(1), int64(4), object(13)
memory usage: 548.6+ KB
None
Customer ID Age Purchase Amount (USD) Review Rating \
count 3900.000000 3900.000000 3900.000000 3900.000000
mean 1950.500000 44.068462 59.764359 3.749949
std 1125.977353 15.207589 23.685392 0.716223
min 1.000000 18.000000 20.000000 2.500000
25% 975.750000 31.000000 39.000000 3.100000
50% 1950.500000 44.000000 60.000000 3.700000
75% 2925.250000 57.000000 81.000000 4.400000
max 3900.000000 70.000000 100.000000 5.000000
Previous Purchases
count 3900.000000
mean 25.351538
std 14.447125
min 1.000000
25% 13.000000
50% 25.000000
75% 38.000000
max 50.000000
Section 3: Common Feature Engineering (Base Transformations)¶
In [56]:
# Mapping categorical frequencies to numerical values
frequency_mapping = {
"Weekly": 52,
"Fortnightly": 26,
"Bi-Weekly": 24,
"Monthly": 12,
"Every 3 Months": 4,
"Quarterly": 4,
"Annually": 1
}
# New column with numeric frequency values
df["Frequency of Purchases (Numeric)"] = df["Frequency of Purchases"].map(frequency_mapping)
# RFM base features that all models will need
df['Recency'] = df['Previous Purchases']
df['Frequency'] = df['Frequency of Purchases (Numeric)']
df['Monetary'] = df['Purchase Amount (USD)']
# Engagement Score based on review rating
if "Review Rating" in df.columns:
df["Engagement_Score"] = df["Review Rating"]
else:
df["Engagement_Score"] = 0 # Default if missing
# Binary subscription status
df["Subscription Status"] = df["Subscription Status"].str.lower().str.strip()
df["Subscription_Status_Binary"] = df["Subscription Status"].apply(lambda x: 1 if x == "subscribed" else 0)
# Loyalty Score as a combination of metrics
df["Loyalty_Score"] = df["Frequency"] + df["Recency"] + (df["Subscription_Status_Binary"] * 10)
# Original DataFrame with basic feature engineering for future models
df_base = df.copy()
In [171]:
# Range of values for each of the RFM feature
print("Recency:", df['Recency'].min(), df['Recency'].max())
print("Frequency:", df['Frequency'].min(), df['Frequency'].max())
print("Monetary:", df['Monetary'].min(), df['Monetary'].max())
print("Engagement Score:", df['Engagement_Score'].min(), df['Engagement_Score'].max())
print("Loyalty Score:", df['Loyalty_Score'].min(), df['Loyalty_Score'].max())
Recency: 1 50 Frequency: 1 52 Monetary: 20 100 Engagement Score: 2.5 5.0 Loyalty Score: 2 102
Recency: 1-50
Scale: Days since last purchase
Interpretation: 1 = purchased yesterday, 50 = purchased ~7 weeks ago
Lower values indicate more recent customers who are currently active.
Frequency: 1-52
Scale: Number of purchases in the tracking period
Interpretation: 1 = one-time buyer, 52 = weekly shopper (approximately)
Higher values represent regular, repeat customers.
Monetary: 20-100
Scale: Average spend in currency units (likely dollars)
Interpretation: 20 = low-value purchases, 100 = high-value purchases
Higher values represent customers who spend more per transaction.
Engagement Score: 2.5-5.0
Scale: Composite metric on a 5-point scale
Interpretation: 2.5 = minimal engagement, 5.0 = maximum engagement
Higher values reflect customers who interact more with emails, website, app, etc.
Loyalty Score: 2-102
Scale: Composite metric combining other factors
Interpretation: 2 = new or infrequent customer, 102 = highly loyal customer
Higher values identify the most valuable long-term customers.
Section 4: K-means Clustering Analysis¶
In [57]:
# Separate copy for K-means modeling
df_kmeans = df_base.copy()
# Feature preparation for K-means. Categorical variables to lowercase for consistency
categorical_cols = ['Gender', 'Category']
for col in categorical_cols:
if col in df_kmeans.columns:
df_kmeans[col] = df_kmeans[col].str.lower().str.strip()
# Feature selection and standardization for K-means
features_for_kmeans = ['Recency', 'Frequency', 'Monetary']
scaler_kmeans = StandardScaler()
df_kmeans_scaled = scaler_kmeans.fit_transform(df_kmeans[features_for_kmeans])
# Optimal K using Elbow Method
inertia = []
k_range = range(1, 11)
for k in k_range:
kmeans = KMeans(n_clusters=k, random_state=42, n_init=10)
kmeans.fit(df_kmeans_scaled)
inertia.append(kmeans.inertia_)
# Plot
plt.figure(figsize=(8, 5))
plt.plot(k_range, inertia, marker="o")
plt.xlabel("Number of Clusters (k)")
plt.ylabel("Inertia")
plt.title("Elbow Method for Optimal K")
plt.show()
# K-means with optimal k
optimal_k = 5 # Identified from elbow plot
kmeans_model = KMeans(n_clusters=optimal_k, random_state=42, n_init=10)
cluster_labels = kmeans_model.fit_predict(df_kmeans_scaled)
# Cluster labels to the dataframe
df_kmeans["KMeans_Cluster"] = cluster_labels
# Analysis of K-means Clusters
# DataFrame with scaled features for analysis
df_kmeans_analysis = pd.DataFrame(df_kmeans_scaled, columns=features_for_kmeans)
df_kmeans_analysis["KMeans_Cluster"] = cluster_labels
# Calculating cluster means
cluster_means = df_kmeans_analysis.groupby("KMeans_Cluster").mean()
print("Cluster means (scaled features):")
print(cluster_means)
# Evaluation metrics
silhouette = silhouette_score(df_kmeans_scaled, cluster_labels)
davies_bouldin = davies_bouldin_score(df_kmeans_scaled, cluster_labels)
inertia_final = kmeans_model.inertia_
print(f"Silhouette Score: {silhouette:.4f}")
print(f"Davies-Bouldin Index: {davies_bouldin:.4f}")
print(f"Inertia (WCSS): {inertia_final:.4f}")
# Cluster characteristics
plt.figure(figsize=(12, 6))
sns.heatmap(cluster_means, annot=True, cmap="coolwarm")
plt.title("K-Means Cluster Centers (RFME Features)")
plt.show()
# Distribution of spending by cluster
plt.figure(figsize=(12, 6))
sns.boxplot(x="KMeans_Cluster", y="Monetary", data=df_kmeans)
plt.title("Spending Distribution Across Clusters")
plt.show()
# Cluster Profiling, defining personas based on cluster characteristics
kmeans_personas = {
0: "High-Value Loyal Customer (High monetary spend with consistent frequency)",
1: "Recent Engaged Shopper (Good engagement score with recent purchases)",
2: "Frequent Low-Spender (High frequency but low monetary value per transaction)",
3: "Infrequent Low-Spender (Low frequency and low monetary value)",
4: "Dormant High-Value Customer (High recency, meaning they haven't purchased in a while)"
}
df_kmeans["KMeans_Persona"] = df_kmeans["KMeans_Cluster"].map(kmeans_personas)
# Summarize profiles with labels
cluster_profiles_kmeans = df_kmeans.groupby("KMeans_Cluster")[features_for_kmeans + ["Loyalty_Score", "Engagement_Score"]].mean().reset_index()
cluster_profiles_kmeans["Persona"] = cluster_profiles_kmeans["KMeans_Cluster"].map(kmeans_personas)
print("\nK-Means Cluster Profiles:")
print(cluster_profiles_kmeans)
Cluster means (scaled features):
Recency Frequency Monetary
KMeans_Cluster
0 0.029093 2.087009 -0.033451
1 -0.848697 -0.334167 -0.877035
2 0.852992 -0.343670 -0.877307
3 0.890113 -0.326187 0.872300
4 -0.863387 -0.335100 0.861021
Silhouette Score: 0.3147
Davies-Bouldin Index: 0.9893
Inertia (WCSS): 3910.6439
K-Means Cluster Profiles: KMeans_Cluster Recency Frequency Monetary Loyalty_Score \ 0 0 25.771800 52.000000 58.972171 77.771800 1 1 13.091873 11.617197 38.994111 24.709069 2 2 37.673243 11.458693 38.987670 49.131936 3 3 38.209467 11.750296 80.422485 49.959763 4 4 12.879673 11.601636 80.155374 24.481308 Engagement_Score Persona 0 3.759555 High-Value Loyal Customer (High monetary spend... 1 3.713310 Recent Engaged Shopper (Good engagement score ... 2 3.711837 Frequent Low-Spender (High frequency but low m... 3 3.782485 Infrequent Low-Spender (Low frequency and low ... 4 3.784229 Dormant High-Value Customer (High recency, mea...
Section 5: Hierarchical Clustering Analysis¶
In [58]:
# Separate copy for hierarchical clustering
df_hierarchical = df_base.copy()
# Feature selection for hierarchical clustering
features_hierarchical = ['Recency', 'Frequency', 'Monetary']
# Feature scaling
scaler_hierarchical = StandardScaler()
df_hierarchical_scaled = scaler_hierarchical.fit_transform(df_hierarchical[features_hierarchical])
# Dendrogram to determine the number of clusters
plt.figure(figsize=(12, 6))
dendrogram = sch.dendrogram(sch.linkage(df_hierarchical_scaled, method="ward"))
plt.title("Dendrogram for Hierarchical Clustering")
plt.xlabel("Customers")
plt.ylabel("Euclidean Distance")
plt.show()
# Number of clusters based on the dendrogram
n_clusters_hierarchical = 4
# Agglomerative Clustering
agg_clustering = AgglomerativeClustering(n_clusters=n_clusters_hierarchical, metric="euclidean", linkage="ward")
df_hierarchical["Hierarchical_Cluster"] = agg_clustering.fit_predict(df_hierarchical_scaled)
# Evaluating the clustering
silhouette_hierarchical = silhouette_score(df_hierarchical_scaled, df_hierarchical["Hierarchical_Cluster"])
davies_bouldin_hierarchical = davies_bouldin_score(df_hierarchical_scaled, df_hierarchical["Hierarchical_Cluster"])
# Clustering metrics
print(f"Silhouette Score (Hierarchical): {silhouette_hierarchical:.4f}")
print(f"Davies-Bouldin Index (Hierarchical): {davies_bouldin_hierarchical:.4f}")
# Cluster distribution
numeric_columns = df_hierarchical.select_dtypes(include=[np.number]).columns
print("\nHierarchical Clustering - Cluster Summary:")
print(df_hierarchical.groupby("Hierarchical_Cluster")[numeric_columns].mean())
# Visualizing cluster characteristics
cluster_means_hierarchical = df_hierarchical.groupby("Hierarchical_Cluster")[features_hierarchical].mean()
plt.figure(figsize=(12, 6))
sns.heatmap(cluster_means_hierarchical, annot=True, cmap="coolwarm")
plt.title("Cluster Profiles (Hierarchical Clustering)")
plt.show()
# Distribution of spending by cluster
plt.figure(figsize=(12, 6))
sns.boxplot(x="Hierarchical_Cluster", y="Monetary", data=df_hierarchical)
plt.title("Monetary Spend Distribution Across Clusters (Hierarchical)")
plt.show()
# Defining personas based on cluster behaviors
hierarchical_personas = {
0: "Premium Shopper (High spending and good frequency, indicating premium buyers.)",
1: "Occasional Buyer (They spend less per transaction and have high recency, meaning they haven't purchased recently but may return occasionally.)",
2: "At-Risk Customer (Low engagement, low monetary spend, and moderate frequency. At risk of churning soon.)",
3: "High-Frequency Budget Shopper(Very high frequency, but moderate spending. They buy often but prioritize affordability over premium products.)"
}
df_hierarchical["Hierarchical_Persona"] = df_hierarchical["Hierarchical_Cluster"].map(hierarchical_personas)
# Summarize profiles
cluster_profiles_hierarchical = df_hierarchical.groupby("Hierarchical_Cluster")[features_hierarchical].mean().reset_index()
cluster_profiles_hierarchical["Persona"] = cluster_profiles_hierarchical["Hierarchical_Cluster"].map(hierarchical_personas)
print("\nHierarchical Clustering - Customer Personas:")
print(cluster_profiles_hierarchical)
Silhouette Score (Hierarchical): 0.2619
Davies-Bouldin Index (Hierarchical): 1.0958
Hierarchical Clustering - Cluster Summary:
Customer ID Age Purchase Amount (USD) \
Hierarchical_Cluster
0 1945.623362 43.951567 41.274644
1 1911.705009 44.649351 58.972171
2 1942.066085 44.099751 74.965087
3 1995.565920 43.902985 85.492537
Review Rating Previous Purchases \
Hierarchical_Cluster
0 3.727464 22.444444
1 3.759555 25.771800
2 3.773317 40.286783
3 3.769279 16.517413
Frequency of Purchases (Numeric) Recency Frequency \
Hierarchical_Cluster
0 11.807977 22.444444 11.807977
1 52.000000 25.771800 52.000000
2 12.375312 40.286783 12.375312
3 10.407960 16.517413 10.407960
Monetary Engagement_Score Subscription_Status_Binary \
Hierarchical_Cluster
0 41.274644 3.727464 0.0
1 58.972171 3.759555 0.0
2 74.965087 3.773317 0.0
3 85.492537 3.769279 0.0
Loyalty_Score Hierarchical_Cluster
Hierarchical_Cluster
0 34.252422 0.0
1 77.771800 1.0
2 52.662095 2.0
3 26.925373 3.0
Hierarchical Clustering - Customer Personas:
Hierarchical_Cluster Recency Frequency Monetary \
0 0 22.444444 11.807977 41.274644
1 1 25.771800 52.000000 58.972171
2 2 40.286783 12.375312 74.965087
3 3 16.517413 10.407960 85.492537
Persona
0 Premium Shopper (High spending and good freque...
1 Occasional Buyer (They spend less per transact...
2 At-Risk Customer (Low engagement, low monetary...
3 High-Frequency Budget Shopper(Very high freque...
Section 6: DBSCAN (Density-Based Clustering)¶
In [59]:
# Separate copy for DBSCAN clustering
df_dbscan = df_base.copy()
# Feature selection for DBSCAN
features_dbscan = ['Recency', 'Frequency', 'Monetary']
# Feature scaling using StandardScaler
scaler_dbscan = StandardScaler()
df_dbscan_scaled = scaler_dbscan.fit_transform(df_dbscan[features_dbscan])
# K-distance graph to determine epsilon
neighbors = NearestNeighbors(n_neighbors=2)
neighbors_fit = neighbors.fit(df_dbscan_scaled)
distances, indices = neighbors_fit.kneighbors(df_dbscan_scaled)
# Distances
distances = np.sort(distances, axis=0)
distances = distances[:, 1]
plt.figure(figsize=(12, 6))
plt.plot(distances)
plt.title("K-Distance Graph")
plt.xlabel("Data Points")
plt.ylabel("Epsilon")
plt.show()
# Fine-tuning around best found parameters
eps_values = [0.58, 0.6, 0.62]
min_samples_values = [5, 8, 10]
dbscan_optimized_results = {}
for eps in eps_values:
for min_samples in min_samples_values:
dbscan = DBSCAN(eps=eps, min_samples=min_samples)
clusters = dbscan.fit_predict(df_dbscan_scaled)
num_clusters = len(set(clusters)) - (1 if -1 in clusters else 0)
num_noise = list(clusters).count(-1)
# Silhouette Score
if num_clusters > 1:
silhouette = silhouette_score(df_dbscan_scaled, clusters)
else:
silhouette = None
dbscan_optimized_results[(eps, min_samples)] = {
"Number of Clusters": num_clusters,
"Noise Points": num_noise,
"Silhouette Score": silhouette
}
# DBSCAN parameter tuning results
print("\nDBSCAN Parameter Tuning Results:")
for params, results in dbscan_optimized_results.items():
print(f"Parameters: {params} - Clusters: {results['Number of Clusters']}, Noise Points: {results['Noise Points']}")
# DBSCAN with optimal parameters
optimal_eps = 0.62
optimal_min_samples = 5
dbscan = DBSCAN(eps=optimal_eps, min_samples=optimal_min_samples)
# Add cluster labels
df_dbscan["DBSCAN_Cluster"] = dbscan.fit_predict(df_dbscan_scaled)
# Count clusters and noise points (-1 indicates noise)
cluster_counts = df_dbscan["DBSCAN_Cluster"].value_counts()
outliers_count = cluster_counts[-1] if -1 in cluster_counts else 0
# Results
print("\nDBSCAN Clustering Results:")
print("Cluster Distribution:\n", cluster_counts)
print("\nNumber of Outliers Detected:", outliers_count)
# Evaluate DBSCAN clustering excluding noise points
filtered_df_dbscan = df_dbscan[df_dbscan["DBSCAN_Cluster"] != -1]
filtered_scaled_dbscan = df_dbscan_scaled[df_dbscan["DBSCAN_Cluster"] != -1]
# Silhouette score
if len(set(filtered_df_dbscan["DBSCAN_Cluster"])) > 1:
silhouette_dbscan = silhouette_score(filtered_scaled_dbscan, filtered_df_dbscan["DBSCAN_Cluster"])
print(f"\nSilhouette Score for DBSCAN: {silhouette_dbscan:.4f}")
else:
silhouette_dbscan = None
print("\nSilhouette Score not available (DBSCAN found only one cluster).")
# DBSCAN clustering results
plt.figure(figsize=(10, 6))
sns.scatterplot(x=df_dbscan["Recency"], y=df_dbscan["Monetary"], hue=df_dbscan["DBSCAN_Cluster"], palette="tab10")
plt.title("DBSCAN Clustering Results (Recency vs Monetary)")
plt.show()
# Personas for DBSCAN clusters
dbscan_personas = {
-1: "Noise/Outliers (Unique customer behaviors that don't fit standard patterns)",
0: "Premium Loyalist (High monetary value, moderate frequency, and high engagement.)",
1: "High-Frequency Deal Seeker (This group buys very frequently but spends moderately, likely prioritizing discounts and promotions.)",
2: "Low-Value Irregular Shopper (They have low spending, low frequency, and are not very engaged, meaning they shop infrequently and don't interact much.)"
}
df_dbscan["DBSCAN_Persona"] = df_dbscan["DBSCAN_Cluster"].map(dbscan_personas)
# Profile summary with persona labels
dbscan_profiles = df_dbscan.groupby("DBSCAN_Cluster")[features_dbscan].mean().reset_index()
dbscan_profiles["Persona"] = dbscan_profiles["DBSCAN_Cluster"].map(dbscan_personas)
print("\nDBSCAN Customer Personas:")
print(dbscan_profiles)
DBSCAN Parameter Tuning Results: Parameters: (0.58, 5) - Clusters: 3, Noise Points: 0 Parameters: (0.58, 8) - Clusters: 3, Noise Points: 0 Parameters: (0.58, 10) - Clusters: 3, Noise Points: 0 Parameters: (0.6, 5) - Clusters: 3, Noise Points: 0 Parameters: (0.6, 8) - Clusters: 3, Noise Points: 0 Parameters: (0.6, 10) - Clusters: 3, Noise Points: 0 Parameters: (0.62, 5) - Clusters: 3, Noise Points: 0 Parameters: (0.62, 8) - Clusters: 3, Noise Points: 0 Parameters: (0.62, 10) - Clusters: 3, Noise Points: 0 DBSCAN Clustering Results: Cluster Distribution: DBSCAN_Cluster 2 2272 0 1089 1 539 Name: count, dtype: int64 Number of Outliers Detected: 0 Silhouette Score for DBSCAN: 0.1924
DBSCAN Customer Personas:
DBSCAN_Cluster Recency Frequency Monetary \
0 0 25.028466 24.995409 59.877870
1 1 25.771800 52.000000 58.972171
2 2 25.406690 5.191901 59.897887
Persona
0 Premium Loyalist (High monetary value, moderat...
1 High-Frequency Deal Seeker (This group buys ve...
2 Low-Value Irregular Shopper (They have low spe...
Section 7: RFME Analysis (Recency, Frequency, Monetary, Engagement)¶
In [60]:
# Copy for RFME analysis
df_rfme = df_base.copy()
# Feature selection for RFME model
rfme_features = ["Recency", "Frequency", "Monetary", "Engagement_Score"]
# Normalizing the RFME features knowing min-max scaling is good for this analysis
scaler_rfme = MinMaxScaler()
rfme_scaled_array = scaler_rfme.fit_transform(df_rfme[rfme_features])
df_rfme_scaled = pd.DataFrame(rfme_scaled_array, columns=rfme_features)
print("\nRFME Features (Normalized):")
print(df_rfme_scaled.head())
# K-means for RFME Clustering
# Elbow Method to determine optimal K
inertia_rfme = []
for k in range(1, 11):
kmeans_rfme = KMeans(n_clusters=k, random_state=42, n_init=10)
kmeans_rfme.fit(df_rfme_scaled)
inertia_rfme.append(kmeans_rfme.inertia_)
# Elbow Method
plt.figure(figsize=(8, 5))
plt.plot(range(1, 11), inertia_rfme, marker="o")
plt.xlabel("Number of Clusters (k)")
plt.ylabel("Inertia")
plt.title("Elbow Method for Optimal K (RFME)")
plt.show()
# Optimal RFME clustering (6 clusters based on visualization)
optimal_k_rfme = 6
kmeans_rfme = KMeans(n_clusters=optimal_k_rfme, random_state=42, n_init=10)
df_rfme["RFME_Cluster"] = kmeans_rfme.fit_predict(df_rfme_scaled)
# Evaluate RFME clustering
silhouette_rfme = silhouette_score(df_rfme_scaled, df_rfme["RFME_Cluster"])
davies_bouldin_rfme = davies_bouldin_score(df_rfme_scaled, df_rfme["RFME_Cluster"])
print("\nRFME Clustering Evaluation:")
print(f"Silhouette Score: {silhouette_rfme:.4f}")
print(f"Davies-Bouldin Index: {davies_bouldin_rfme:.4f}")
# RFME Cluster Profiles
cluster_means_rfme = df_rfme.groupby("RFME_Cluster")[rfme_features].mean()
plt.figure(figsize=(10, 6))
sns.heatmap(cluster_means_rfme, annot=True, cmap="viridis")
plt.title("RFME Cluster Profiles")
plt.show()
# RFME Cluster Personas
rfme_personas = {
0: "High-Value Loyal Customer (High monetary spend and engagement, moderate frequency, and moderate recency. They are high-value customers who consistently engage with the brand.)",
1: "Recent Engaged Shopper (High engagement score and low recency, meaning they are actively shopping but not frequently. They may become more loyal over time.)",
2: "Frequent Low-Spender (Extremely high frequency but low monetary spend. They buy very often but in small amounts.)",
3: "Infrequent Low-Spender (Low monetary and moderate engagement, meaning they buy occasionally but don't spend much.)",
4: "Dormant High-Value Customer (High recency (meaning they haven't purchased in a while), but decent frequency and spending when they were active. They may be worth re-engaging.)",
5: "High-Frequency Premium Buyer (High frequency and monetary spend, meaning they shop often and spend well, but their engagement score is slightly lower.)"
}
df_rfme["RFME_Persona"] = df_rfme["RFME_Cluster"].map(rfme_personas)
# Cluster profiles with persona descriptions
cluster_summary_rfme = df_rfme.groupby("RFME_Cluster")[rfme_features].mean().reset_index()
cluster_summary_rfme["Persona"] = cluster_summary_rfme["RFME_Cluster"].map(rfme_personas)
print("\nRFME Cluster Summary:")
print(cluster_summary_rfme)
# RFME Segments
plt.figure(figsize=(12, 5))
sns.scatterplot(x="Recency", y="Monetary", hue="RFME_Persona", data=df_rfme, palette="tab10")
plt.title("RFME Customer Segments")
plt.show()
RFME Features (Normalized):
Recency Frequency Monetary Engagement_Score
0 0.265306 0.490196 0.4125 0.24
1 0.020408 0.490196 0.5500 0.24
2 0.448980 1.000000 0.6625 0.24
3 0.979592 1.000000 0.8750 0.40
4 0.612245 0.000000 0.3625 0.08
RFME Clustering Evaluation: Silhouette Score: 0.2246 Davies-Bouldin Index: 1.2307
RFME Cluster Summary:
RFME_Cluster Recency Frequency Monetary Engagement_Score \
0 0 37.868243 11.608108 79.815878 4.329392
1 1 11.694405 11.319943 61.969871 4.427260
2 2 25.771800 52.000000 58.972171 3.759555
3 3 13.551020 11.457483 38.957483 3.222449
4 4 38.305292 11.502035 37.633650 3.716147
5 5 24.380187 12.101740 80.599732 3.100402
Persona
0 High-Value Loyal Customer (High monetary spend...
1 Recent Engaged Shopper (High engagement score ...
2 Frequent Low-Spender (Extremely high frequency...
3 Infrequent Low-Spender (Low monetary and moder...
4 Dormant High-Value Customer (High recency (mea...
5 High-Frequency Premium Buyer (High frequency a...
Section 8: Gaussian Mixture Models (GMM)¶
In [61]:
# Copy for GMM analysis
df_gmm = df_base.copy()
# Feature selection and scaling for GMM
features_gmm = ['Recency', 'Frequency', 'Monetary', 'Engagement_Score']
scaler_gmm = StandardScaler()
df_gmm_scaled = scaler_gmm.fit_transform(df_gmm[features_gmm])
# PCA to reduce dimensionality before GMM
pca_gmm = PCA(n_components=2)
X_pca_gmm = pca_gmm.fit_transform(df_gmm_scaled)
# Optimal number of components for GMM using BIC
n_components_range = range(2, 10)
bic_scores = []
aic_scores = []
gmm_models = {}
for n_components in n_components_range:
gmm = GaussianMixture(n_components=n_components, random_state=42)
gmm.fit(X_pca_gmm)
bic_scores.append(gmm.bic(X_pca_gmm))
aic_scores.append(gmm.aic(X_pca_gmm))
gmm_models[n_components] = gmm
# Optimal number of components, looking for the lowest BIC score
optimal_n_components = n_components_range[np.argmin(bic_scores)]
print(f"\nOptimal Number of GMM Components (using BIC): {optimal_n_components}")
# Alternative numbers of components for comparison
alternative_components = [5] # Testing 5 components based on results from other models
gmm_alternative_results = {}
for n_components in alternative_components:
gmm = GaussianMixture(n_components=n_components, random_state=42)
gmm.fit(X_pca_gmm)
labels = gmm.predict(X_pca_gmm)
# Silhouette score
silhouette = silhouette_score(X_pca_gmm, labels)
gmm_alternative_results[n_components] = {"Silhouette Score": silhouette}
# Alternative results
for n_components, results in gmm_alternative_results.items():
print(f"GMM with {n_components} Components - Silhouette Score: {results['Silhouette Score']:.4f}")
# GMM with 5 components (more interpretable)
gmm_final = GaussianMixture(n_components=5, random_state=42)
gmm_final.fit(X_pca_gmm)
df_gmm["GMM_Cluster"] = gmm_final.predict(X_pca_gmm)
# Silhouette score
silhouette_gmm = silhouette_score(X_pca_gmm, df_gmm["GMM_Cluster"])
print(f"GMM Final Silhouette Score: {silhouette_gmm:.4f}")
# GMM Clusters
plt.figure(figsize=(8, 6))
sns.scatterplot(x=X_pca_gmm[:, 0], y=X_pca_gmm[:, 1], hue=df_gmm["GMM_Cluster"], palette="viridis")
plt.title("GMM Clustering Results (PCA)")
plt.show()
# GMM personas based on cluster characteristics
gmm_profiles = df_gmm.groupby("GMM_Cluster")[features_gmm].mean()
print("\nGMM Cluster Profiles:")
print(gmm_profiles)
gmm_personas = {
0: "Low Engagement Browser (Low across all metrics, particularly engagement score. These customers browse occasionally but rarely convert to significant purchases.)",
1: "Moderate Value Loyal (Medium recency, frequency, and monetary values with good engagement. These are solid middle-tier customers.)",
2: "High-Value Premium (High monetary value with good recency and frequency. These are premium customers who make regular valuable purchases.)",
3: "Recent Low-Value Explorer (Low recency (recent purchases) but low monetary value. These may be new customers exploring the brand with small initial purchases.)",
4: "Occasional Big Spender (High monetary value but low frequency. These customers shop infrequently but spend significantly when they do.)"
}
df_gmm["GMM_Persona"] = df_gmm["GMM_Cluster"].map(gmm_personas)
# Summary with persona descriptions
gmm_summary = df_gmm.groupby("GMM_Cluster")[features_gmm].mean().reset_index()
gmm_summary["Persona"] = gmm_summary["GMM_Cluster"].map(gmm_personas)
print("\nGMM Persona Summary:")
print(gmm_summary)
Optimal Number of GMM Components (using BIC): 4 GMM with 5 Components - Silhouette Score: 0.3268 GMM Final Silhouette Score: 0.3268
GMM Cluster Profiles:
Recency Frequency Monetary Engagement_Score
GMM_Cluster
0 25.500556 8.129032 81.880979 4.314127
1 30.163636 15.282828 57.055556 3.732727
2 39.261792 38.353774 67.943396 4.166038
3 12.737978 8.368008 50.680079 3.375859
4 28.973592 34.889085 39.672535 3.247535
GMM Persona Summary:
GMM_Cluster Recency Frequency Monetary Engagement_Score \
0 0 25.500556 8.129032 81.880979 4.314127
1 1 30.163636 15.282828 57.055556 3.732727
2 2 39.261792 38.353774 67.943396 4.166038
3 3 12.737978 8.368008 50.680079 3.375859
4 4 28.973592 34.889085 39.672535 3.247535
Persona
0 Low Engagement Browser (Low across all metrics...
1 Moderate Value Loyal (Medium recency, frequenc...
2 High-Value Premium (High monetary value with g...
3 Recent Low-Value Explorer (Low recency (recent...
4 Occasional Big Spender (High monetary value bu...
Section 9: K-Prototypes for Mixed Data Types¶
In [121]:
# Separate copy for K-Prototypes analysis
df_kproto = df_base.copy()
# Features for K-Prototypes
categorical_features = ['Gender', 'Category', 'Size', 'Color', 'Season', 'Subscription Status', 'Payment Method']
numerical_features = ['Age', 'Frequency', 'Monetary', 'Engagement_Score', 'Recency']
# Checking that the categorical features are strings
df_kproto[categorical_features] = df_kproto[categorical_features].astype(str)
# Encoding categorical features
from sklearn.preprocessing import LabelEncoder
label_encoders = {}
for col in categorical_features:
le = LabelEncoder()
df_kproto[col] = le.fit_transform(df_kproto[col])
label_encoders[col] = le
# Preparing data for K-Prototypes
X_kproto = df_kproto[numerical_features + categorical_features].values
categorical_indices = [len(numerical_features) + i for i in range(len(categorical_features))]
# K-Prototypes clustering
kproto = KPrototypes(n_clusters=4, init='Huang', random_state=42)
clusters = kproto.fit_predict(X_kproto, categorical=categorical_indices)
df_kproto['KPrototypes_Cluster'] = clusters
# KPrototypes clusters
print("\nK-Prototypes Clustering Results:")
print(df_kproto[["Customer ID", "KPrototypes_Cluster"]].head())
# Compute mean values for numerical features per cluster
cluster_means_kproto = df_kproto.groupby("KPrototypes_Cluster")[numerical_features].mean()
# Cluster profiles
plt.figure(figsize=(10, 6))
sns.heatmap(cluster_means_kproto, annot=True, cmap="coolwarm")
plt.title("K-Prototypes Cluster Profiles (Numerical Features)")
plt.show()
# Getting mode (most frequent category) for categorical features in each cluster
cluster_modes_kproto = df_kproto.groupby("KPrototypes_Cluster")[categorical_features].agg(lambda x: x.mode()[0])
print("\nMost Common Categorical Values per Cluster:")
print(cluster_modes_kproto)
# K-Prototypes personas
kproto_personas = {
0: "Luxury High-Spender (High frequency and moderate monetary spend, suggesting they are engaged, frequent shoppers who make regular purchases.)",
1: "Infrequent High-Spender (Older age with moderate spending but low frequency, indicating they buy less often but in higher amounts.)",
2: "Loyal Subscription Shopper (Moderate frequency, moderate spend, and steady engagement suggest customers who consistently purchase—likely through subscriptions or repeat orders.)",
3: "Moderate-Frequency Engaged Shopper (Lower recency and decent engagement, meaning they may shop semi-regularly but without strong spending patterns.)"
}
# Map personas to clusters
df_kproto["KPrototypes_Persona"] = df_kproto["KPrototypes_Cluster"].map(kproto_personas)
# Display persona summary
cluster_summary_kproto = df_kproto.groupby("KPrototypes_Cluster")[numerical_features].mean().reset_index()
cluster_summary_kproto["Persona"] = cluster_summary_kproto["KPrototypes_Cluster"].map(kproto_personas)
print("\nK-Prototypes Cluster Summary:")
print(cluster_summary_kproto)
# Silhouette Score for K-Prototypes
silhouette_kproto1 = silhouette_score(X_kproto, clusters)
print(f"\nSilhouette Score for K-Prototypes: {silhouette_kproto1:.4f}")
K-Prototypes Clustering Results: Customer ID KPrototypes_Cluster 0 1 1 1 2 0 2 3 3 3 4 3 4 5 1
Most Common Categorical Values per Cluster:
Gender Category Size Color Season \
KPrototypes_Cluster
0 1 1 1 8 3
1 1 1 1 19 1
2 1 1 1 22 0
3 1 1 1 24 3
Subscription Status Payment Method
KPrototypes_Cluster
0 0 5
1 0 4
2 0 2
3 0 2
K-Prototypes Cluster Summary:
KPrototypes_Cluster Age Frequency Monetary Engagement_Score \
0 0 30.327064 11.933124 77.545455 3.765935
1 1 44.228246 11.661981 37.019411 3.715730
2 2 57.913187 11.179121 78.875824 3.783626
3 3 44.649351 52.000000 58.972171 3.759555
Recency Persona
0 24.938349 Luxury High-Spender (High frequency and modera...
1 25.068942 Infrequent High-Spender (Older age with modera...
2 26.001099 Loyal Subscription Shopper (Moderate frequency...
3 25.771800 Moderate-Frequency Engaged Shopper (Lower rece...
Silhouette Score for K-Prototypes: 0.2310
Silhouette Score for K-Prototypes: 0.2310
In [64]:
# Silhouette score for K-Prototypes
df_kproto_num = df_kproto.copy()
df_kproto_num[categorical_features] = df_kproto_num[categorical_features].astype('category').apply(lambda x: x.cat.codes)
X_kproto_num = df_kproto_num[numerical_features + categorical_features].values
silhouette_kproto = silhouette_score(X_kproto_num, df_kproto["KPrototypes_Cluster"])
print(f"\nK-Prototypes Silhouette Score: {silhouette_kproto:.4f}")
K-Prototypes Silhouette Score: 0.2310
Section 10: Umap¶
In [69]:
# Separate copy for Umap analysis
df_umap = df_base.copy()
df_umap['customer_profile'] = df_umap.apply(
lambda row: f"A {row['Gender']} customer, age {row['Age']}, purchased {row['Item Purchased']} " +
f"from {row['Category']} category, {row['Color']} color, in {row['Season']}, size {row['Size']}. " +
f"Previous purchases: {row['Previous Purchases']}. " +
f"Purchase frequency: {row['Frequency of Purchases']}. " +
f"They spent ${row['Purchase Amount (USD)']}. " +
f"Rating: {row['Review Rating']}/5. " +
f"Discount applied: {row['Discount Applied']}. " +
f"Promo code used: {row['Promo Code Used']}. " +
f"Subscription status: {row['Subscription Status']}. " +
f"Shipping type: {row['Shipping Type']}. " +
f"Payment method: {row['Payment Method']}.",
axis=1
)
# Compute quantile thresholds for Purchase Amount and Previous Purchases
low_purchase = df_umap['Purchase Amount (USD)'].quantile(0.25)
high_purchase = df_umap['Purchase Amount (USD)'].quantile(0.75)
low_prev = df_umap['Previous Purchases'].quantile(0.25)
high_prev = df_umap['Previous Purchases'].quantile(0.75)
def spending_level(amount):
if amount > high_purchase:
return "high spender"
elif amount < low_purchase:
return "budget-conscious spender"
else:
return "moderate spender"
def loyalty_level(prev):
if prev > high_prev:
return "loyal"
elif prev < low_prev:
return "new"
else:
return "returning"
# More diverse customer profile string
df_umap['customer_profile'] = df_umap.apply(
lambda row: (
f"Profile:\n"
f"- Customer: A {row['Gender']} aged {row['Age']} years.\n"
f"- Spending Behavior: A {spending_level(row['Purchase Amount (USD)'])} who spent ${row['Purchase Amount (USD)']:.2f} and made {row['Previous Purchases']} past purchases, thus classified as a {loyalty_level(row['Previous Purchases'])} customer.\n"
f"- Purchase Details: Purchased a {row['Item Purchased']} from the {row['Category']} category, noted for its {row['Color']} color, size {row['Size']}, during {row['Season']} season.\n"
f"- Experience: Rated {row['Review Rating']}/5; shops {row['Frequency of Purchases']}, discount applied: {row['Discount Applied']}, promo code used: {row['Promo Code Used']}.\n"
f"- Engagement: Subscription status: {row['Subscription Status']}, prefers {row['Shipping Type']} shipping, and uses {row['Payment Method']} for payment."
),
axis=1
)
# Create new embeddings using the diversified customer profiles
model = SentenceTransformer('all-MiniLM-L6-v2')
embeddings = model.encode(df_umap['customer_profile'].tolist(), show_progress_bar=True)
# Display a few sample profiles to verify the changes
print(df_umap[['customer_profile']].head())
Batches: 0%| | 0/122 [00:00<?, ?it/s]
customer_profile 0 Profile:\n- Customer: A Male aged 55 years.\n-... 1 Profile:\n- Customer: A Male aged 19 years.\n-... 2 Profile:\n- Customer: A Male aged 50 years.\n-... 3 Profile:\n- Customer: A Male aged 21 years.\n-... 4 Profile:\n- Customer: A Male aged 45 years.\n-...
In [95]:
numeric_cols = ['Age', 'Purchase Amount (USD)', 'Review Rating', 'Previous Purchases', 'Frequency of Purchases (Numeric)']
scaler = StandardScaler()
numeric_scaled = scaler.fit_transform(df_umap[numeric_cols])
embeddings_numeric = np.concatenate([embeddings, numeric_scaled], axis=1)
# Sample profiles to verify the changes
print(df_umap[['customer_profile']].head())
customer_profile 0 Profile:\n- Customer: A Male aged 55 years.\n-... 1 Profile:\n- Customer: A Male aged 19 years.\n-... 2 Profile:\n- Customer: A Male aged 50 years.\n-... 3 Profile:\n- Customer: A Male aged 21 years.\n-... 4 Profile:\n- Customer: A Male aged 45 years.\n-...
In [97]:
import umap.umap_ as umap
# Use UMAP to reduce dimensionality for visualization and clustering
print("Reducing dimensionality...")
reducer = umap.UMAP(n_components=2, metric='cosine', random_state=42)
umap_embeddings = reducer.fit_transform(embeddings_numeric)
combined_features = np.hstack((embeddings, numeric_scaled))
Reducing dimensionality...
/opt/anaconda3/lib/python3.12/site-packages/umap/umap_.py:1952: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism. warn(
In [98]:
# Optimal number of clusters
print("Finding optimal number of clusters...")
silhouette_scores = []
for k in range(2, 11):
kmeans = KMeans(n_clusters=k, random_state=42, n_init=10)
cluster_labels = kmeans.fit_predict(combined_features)
silhouette_scores.append(silhouette_score(combined_features, cluster_labels))
# Silhouette scores
plt.figure(figsize=(10, 6))
plt.plot(range(2, 11), silhouette_scores, 'o-')
plt.xlabel('Number of Clusters')
plt.ylabel('Silhouette Score')
plt.title('Silhouette Scores by Cluster Count')
plt.grid(True)
plt.savefig('llm_silhouette_scores.png')
Finding optimal number of clusters...
In [101]:
# Best K based on silhouette score
best_k = np.argmax(silhouette_scores) + 2
print(f"Optimal number of clusters: {best_k} (Silhouette Score: {silhouette_scores[best_k-2]:.4f})")
# K-means with optimal K
print(f"Clustering with K={best_k}...")
kmeans = KMeans(n_clusters=best_k, random_state=42, n_init=10)
df['Cluster'] = kmeans.fit_predict(combined_features)
Optimal number of clusters: 10 (Silhouette Score: 0.1771) Clustering with K=10...
In [102]:
# 2D UMAP projection for visualization
viz_reducer = umap.UMAP(n_components=2, metric='cosine', random_state=42)
viz_embedding = viz_reducer.fit_transform(embeddings_numeric)
df['UMAP1'] = viz_embedding[:, 0]
df['UMAP2'] = viz_embedding[:, 1]
# Profile the clusters
segment_stats = df.groupby('Cluster').agg({
'Customer ID': 'count',
'Purchase Amount (USD)': 'mean',
'Age': 'mean',
'Previous Purchases': 'mean',
'Review Rating': 'mean'
}).round(2)
segment_stats['Percentage'] = (segment_stats['Customer ID'] / len(df) * 100).round(1)
segment_stats = segment_stats.rename(columns={'Customer ID': 'Count'})
print("\nSegment Statistics:")
print(segment_stats)
/opt/anaconda3/lib/python3.12/site-packages/umap/umap_.py:1952: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism. warn(
Segment Statistics:
Count Purchase Amount (USD) Age Previous Purchases \
Cluster
0 405 39.65 55.91 35.93
1 429 78.36 32.95 13.55
2 434 41.45 56.64 15.98
3 272 58.74 41.52 14.04
4 457 40.60 30.77 14.32
5 432 44.10 33.79 37.31
6 266 59.07 47.77 37.85
7 383 81.11 57.52 38.05
8 396 78.86 31.09 36.35
9 426 79.40 55.19 14.20
Review Rating Percentage
Cluster
0 4.29 10.4
1 3.16 11.0
2 3.16 11.1
3 3.61 7.0
4 4.26 11.7
5 3.13 11.1
6 3.90 6.8
7 3.37 9.8
8 4.33 10.2
9 4.31 10.9
In [103]:
# Automatically generate segment names using profile features
segment_names = {}
for cluster in range(best_k):
cluster_df = df[df['Cluster'] == cluster]
# Calculate key metrics
avg_spending = cluster_df['Purchase Amount (USD)'].mean()
avg_previous = cluster_df['Previous Purchases'].mean()
avg_rating = cluster_df['Review Rating'].mean()
discount_rate = (cluster_df['Discount Applied'] == 'Yes').mean()
subscription_rate = (cluster_df['Subscription Status'] == 'Yes').mean()
# Top categories, colors, and seasons
top_category = cluster_df['Category'].value_counts().index[0]
top_season = cluster_df['Season'].value_counts().index[0]
top_items = cluster_df['Item Purchased'].value_counts().nlargest(2).index.tolist()
# Determine spending level
if avg_spending > df['Purchase Amount (USD)'].quantile(0.75):
spending = "High-Spending"
elif avg_spending < df['Purchase Amount (USD)'].quantile(0.25):
spending = "Budget-Conscious"
else:
spending = "Mid-Range"
# Determine loyalty level
if avg_previous > df['Previous Purchases'].quantile(0.75):
loyalty = "Loyal"
elif avg_previous < df['Previous Purchases'].quantile(0.25):
loyalty = "New"
else:
loyalty = "Returning"
# Create segment name
if subscription_rate > 0.5:
prefix = "Subscribed"
elif discount_rate > 0.7:
prefix = "Discount-Seeking"
else:
prefix = ""
if prefix:
name = f"{prefix} {loyalty} {spending} Shoppers"
else:
name = f"{loyalty} {spending} Shoppers"
# Add specialty if very focused on one category
if cluster_df['Category'].value_counts(normalize=True).iloc[0] > 0.6:
name += f" ({top_category} Focus)"
segment_names[cluster] = name
In [104]:
# Map cluster numbers to names
df['Segment'] = df['Cluster'].map(segment_names)
# Visualize the clusters
plt.figure(figsize=(14, 10))
scatter = sns.scatterplot(
data=df,
x='UMAP1',
y='UMAP2',
hue='Segment',
palette='viridis',
s=100,
alpha=0.7
)
plt.title('LLM-Based Customer Segments', fontsize=16)
plt.legend(title='Segment', bbox_to_anchor=(1.05, 1), loc='upper left')
plt.tight_layout()
plt.show()
In [105]:
# Generate segment insights and marketing recommendations
print("\n--- SEGMENT PROFILES & MARKETING RECOMMENDATIONS ---")
for cluster, segment_name in segment_names.items():
segment_data = df[df['Cluster'] == cluster]
count = len(segment_data)
pct = count / len(df) * 100
print(f"\n=== {segment_name} ===")
print(f"Size: {count} customers ({pct:.1f}% of total)")
# Calculate key metrics
avg_purchase = segment_data['Purchase Amount (USD)'].mean()
freq_counts = segment_data['Frequency of Purchases'].value_counts().nlargest(2)
avg_previous = segment_data['Previous Purchases'].mean()
avg_rating = segment_data['Review Rating'].mean()
discount_usage = (segment_data['Discount Applied'] == 'Yes').mean() * 100
promo_usage = (segment_data['Promo Code Used'] == 'Yes').mean() * 100
subscription_rate = (segment_data['Subscription Status'] == 'Yes').mean() * 100
# Top categories and products
top_categories = segment_data['Category'].value_counts().nlargest(2)
top_items = segment_data['Item Purchased'].value_counts().nlargest(3)
top_seasons = segment_data['Season'].value_counts().nlargest(2)
top_colors = segment_data['Color'].value_counts().nlargest(3)
# Print segment profile
print("\nKEY CHARACTERISTICS:")
print(f"• Average purchase: ${avg_purchase:.2f}")
print(f"• Purchase frequency: {', '.join([f'{freq} ({count})' for freq, count in freq_counts.items()])}")
print(f"• Previous purchases: {avg_previous:.1f}")
print(f"• Average rating: {avg_rating:.1f}/5")
print(f"• Discount usage: {discount_usage:.1f}%")
print(f"• Promo code usage: {promo_usage:.1f}%")
print(f"• Subscription rate: {subscription_rate:.1f}%")
print("\nPRODUCT PREFERENCES:")
print("• Top categories:")
for cat, count in top_categories.items():
print(f" - {cat}: {count} purchases ({count/len(segment_data)*100:.1f}% of segment)")
print("• Top items:")
for item, count in top_items.items():
print(f" - {item}: {count} purchases")
print("• Top seasons:")
for season, count in top_seasons.items():
print(f" - {season}: {count} purchases")
print("• Top colors:")
for color, count in top_colors.items():
print(f" - {color}: {count} purchases")
--- SEGMENT PROFILES & MARKETING RECOMMENDATIONS --- === Returning Mid-Range Shoppers === Size: 405 customers (10.4% of total) KEY CHARACTERISTICS: • Average purchase: $39.65 • Purchase frequency: Fortnightly (75), Every 3 Months (72) • Previous purchases: 35.9 • Average rating: 4.3/5 • Discount usage: 43.2% • Promo code usage: 43.2% • Subscription rate: 0.0% PRODUCT PREFERENCES: • Top categories: - Clothing: 169 purchases (41.7% of segment) - Accessories: 137 purchases (33.8% of segment) • Top items: - Jewelry: 31 purchases - Blouse: 26 purchases - Gloves: 24 purchases • Top seasons: - Spring: 106 purchases - Summer: 102 purchases • Top colors: - Silver: 23 purchases - Turquoise: 23 purchases - Teal: 23 purchases === Returning Mid-Range Shoppers === Size: 429 customers (11.0% of total) KEY CHARACTERISTICS: • Average purchase: $78.36 • Purchase frequency: Bi-Weekly (83), Every 3 Months (76) • Previous purchases: 13.5 • Average rating: 3.2/5 • Discount usage: 41.7% • Promo code usage: 41.7% • Subscription rate: 0.0% PRODUCT PREFERENCES: • Top categories: - Clothing: 185 purchases (43.1% of segment) - Accessories: 146 purchases (34.0% of segment) • Top items: - Backpack: 23 purchases - Sunglasses: 23 purchases - Shirt: 22 purchases • Top seasons: - Fall: 119 purchases - Winter: 109 purchases • Top colors: - Green: 25 purchases - Teal: 22 purchases - Maroon: 21 purchases === Returning Mid-Range Shoppers === Size: 434 customers (11.1% of total) KEY CHARACTERISTICS: • Average purchase: $41.45 • Purchase frequency: Annually (88), Quarterly (79) • Previous purchases: 16.0 • Average rating: 3.2/5 • Discount usage: 42.9% • Promo code usage: 42.9% • Subscription rate: 0.0% PRODUCT PREFERENCES: • Top categories: - Clothing: 193 purchases (44.5% of segment) - Accessories: 125 purchases (28.8% of segment) • Top items: - Shoes: 24 purchases - Blouse: 23 purchases - Sandals: 23 purchases • Top seasons: - Spring: 119 purchases - Summer: 111 purchases • Top colors: - Blue: 24 purchases - Beige: 24 purchases - Teal: 22 purchases === Returning Mid-Range Shoppers === Size: 272 customers (7.0% of total) KEY CHARACTERISTICS: • Average purchase: $58.74 • Purchase frequency: Weekly (272) • Previous purchases: 14.0 • Average rating: 3.6/5 • Discount usage: 39.3% • Promo code usage: 39.3% • Subscription rate: 0.0% PRODUCT PREFERENCES: • Top categories: - Clothing: 131 purchases (48.2% of segment) - Accessories: 77 purchases (28.3% of segment) • Top items: - Shorts: 18 purchases - Sweater: 16 purchases - Pants: 14 purchases • Top seasons: - Spring: 73 purchases - Fall: 69 purchases • Top colors: - Cyan: 18 purchases - Yellow: 16 purchases - Lavender: 14 purchases === Returning Mid-Range Shoppers === Size: 457 customers (11.7% of total) KEY CHARACTERISTICS: • Average purchase: $40.60 • Purchase frequency: Every 3 Months (88), Fortnightly (81) • Previous purchases: 14.3 • Average rating: 4.3/5 • Discount usage: 40.7% • Promo code usage: 40.7% • Subscription rate: 0.0% PRODUCT PREFERENCES: • Top categories: - Clothing: 196 purchases (42.9% of segment) - Accessories: 159 purchases (34.8% of segment) • Top items: - Belt: 30 purchases - Socks: 25 purchases - Pants: 23 purchases • Top seasons: - Spring: 126 purchases - Summer: 122 purchases • Top colors: - Olive: 34 purchases - Magenta: 26 purchases - Black: 25 purchases === Returning Mid-Range Shoppers === Size: 432 customers (11.1% of total) KEY CHARACTERISTICS: • Average purchase: $44.10 • Purchase frequency: Quarterly (78), Monthly (76) • Previous purchases: 37.3 • Average rating: 3.1/5 • Discount usage: 49.8% • Promo code usage: 49.8% • Subscription rate: 0.0% PRODUCT PREFERENCES: • Top categories: - Clothing: 199 purchases (46.1% of segment) - Accessories: 139 purchases (32.2% of segment) • Top items: - Shorts: 25 purchases - Pants: 24 purchases - Scarf: 24 purchases • Top seasons: - Winter: 123 purchases - Summer: 113 purchases • Top colors: - Green: 25 purchases - Peach: 21 purchases - Blue: 20 purchases === Returning Mid-Range Shoppers === Size: 266 customers (6.8% of total) KEY CHARACTERISTICS: • Average purchase: $59.07 • Purchase frequency: Weekly (266) • Previous purchases: 37.9 • Average rating: 3.9/5 • Discount usage: 47.4% • Promo code usage: 47.4% • Subscription rate: 0.0% PRODUCT PREFERENCES: • Top categories: - Clothing: 110 purchases (41.4% of segment) - Accessories: 89 purchases (33.5% of segment) • Top items: - Sweater: 15 purchases - Coat: 14 purchases - Sunglasses: 14 purchases • Top seasons: - Winter: 80 purchases - Spring: 69 purchases • Top colors: - Turquoise: 18 purchases - Black: 15 purchases - Peach: 15 purchases === Loyal High-Spending Shoppers === Size: 383 customers (9.8% of total) KEY CHARACTERISTICS: • Average purchase: $81.11 • Purchase frequency: Quarterly (79), Every 3 Months (67) • Previous purchases: 38.1 • Average rating: 3.4/5 • Discount usage: 45.2% • Promo code usage: 45.2% • Subscription rate: 0.0% PRODUCT PREFERENCES: • Top categories: - Clothing: 187 purchases (48.8% of segment) - Accessories: 120 purchases (31.3% of segment) • Top items: - Socks: 21 purchases - Dress: 21 purchases - Jewelry: 20 purchases • Top seasons: - Spring: 98 purchases - Summer: 97 purchases • Top colors: - Beige: 22 purchases - Cyan: 22 purchases - Gray: 21 purchases === Returning Mid-Range Shoppers === Size: 396 customers (10.2% of total) KEY CHARACTERISTICS: • Average purchase: $78.86 • Purchase frequency: Quarterly (70), Annually (68) • Previous purchases: 36.4 • Average rating: 4.3/5 • Discount usage: 35.9% • Promo code usage: 35.9% • Subscription rate: 0.0% PRODUCT PREFERENCES: • Top categories: - Clothing: 181 purchases (45.7% of segment) - Accessories: 121 purchases (30.6% of segment) • Top items: - Blouse: 25 purchases - Shirt: 23 purchases - Skirt: 20 purchases • Top seasons: - Spring: 108 purchases - Winter: 100 purchases • Top colors: - Maroon: 24 purchases - White: 24 purchases - Teal: 21 purchases === Returning Mid-Range Shoppers === Size: 426 customers (10.9% of total) KEY CHARACTERISTICS: • Average purchase: $79.40 • Purchase frequency: Annually (79), Monthly (78) • Previous purchases: 14.2 • Average rating: 4.3/5 • Discount usage: 44.1% • Promo code usage: 44.1% • Subscription rate: 0.0% PRODUCT PREFERENCES: • Top categories: - Clothing: 186 purchases (43.7% of segment) - Accessories: 127 purchases (29.8% of segment) • Top items: - Jacket: 31 purchases - T-shirt: 25 purchases - Dress: 23 purchases • Top seasons: - Fall: 117 purchases - Winter: 110 purchases • Top colors: - Yellow: 26 purchases - Green: 24 purchases - Orange: 23 purchases
In [106]:
# Visualize segments bar chart showing segment sizes
plt.figure(figsize=(12, 6))
ax = segment_stats['Count'].sort_values().plot(kind='barh')
plt.title('Customer Segments by Size', fontsize=15)
plt.xlabel('Number of Customers', fontsize=12)
for i, v in enumerate(segment_stats['Count'].sort_values()):
ax.text(v + 10, i, str(v), va='center')
plt.tight_layout()
plt.show()
In [107]:
# Table with the key characteristics per cluster
print("\nSegment Statistics:")
print(segment_stats)
Segment Statistics:
Count Purchase Amount (USD) Age Previous Purchases \
Cluster
0 405 39.65 55.91 35.93
1 429 78.36 32.95 13.55
2 434 41.45 56.64 15.98
3 272 58.74 41.52 14.04
4 457 40.60 30.77 14.32
5 432 44.10 33.79 37.31
6 266 59.07 47.77 37.85
7 383 81.11 57.52 38.05
8 396 78.86 31.09 36.35
9 426 79.40 55.19 14.20
Review Rating Percentage
Cluster
0 4.29 10.4
1 3.16 11.0
2 3.16 11.1
3 3.61 7.0
4 4.26 11.7
5 3.13 11.1
6 3.90 6.8
7 3.37 9.8
8 4.33 10.2
9 4.31 10.9
In [108]:
# Creating a of key metrics by cluster
plt.figure(figsize=(14, 10))
plt.subplot(2, 2, 1)
sns.boxplot(data=df, x='Cluster', y='Purchase Amount (USD)')
plt.title('Purchase Amount by Cluster')
plt.xlabel('Cluster')
plt.ylabel('Purchase Amount (USD)')
plt.subplot(2, 2, 2)
sns.boxplot(data=df, x='Cluster', y='Previous Purchases')
plt.title('Previous Purchases by Cluster')
plt.xlabel('Cluster')
plt.ylabel('Previous Purchases')
plt.subplot(2, 2, 3)
sns.boxplot(data=df, x='Cluster', y='Review Rating')
plt.title('Review Rating by Cluster')
plt.xlabel('Cluster')
plt.ylabel('Review Rating')
plt.tight_layout()
plt.show()
Section 11: Model Comparision¶
In [124]:
# Models comparison
print("\n--- CLUSTERING MODEL COMPARISON ---")
print("Silhouette Scores:")
print(f"K-Means: 0.3147")
print(f"Hierarchical: {silhouette_hierarchical:.4f}")
print(f"DBSCAN: {silhouette_dbscan:.4f}")
print(f"RFME: {silhouette_rfme:.4f}")
print(f"GMM: {silhouette_gmm:.4f}")
print(f"K-Prototypes: {silhouette_kproto:.4f}")
print(f"Umap: {silhouette_scores[best_k-2]:.4f}")
--- CLUSTERING MODEL COMPARISON --- Silhouette Scores: K-Means: 0.3147 Hierarchical: 0.2619 DBSCAN: 0.1924 RFME: 0.2246 GMM: 0.3268 K-Prototypes: 0.2310 Umap: 0.1771
In [128]:
# Models comparison clustering metrics
plt.figure(figsize=(12, 6))
models = ["K-Means", "Hierarchical", "DBSCAN", "RFME", "GMM", "K-Prototypes", "Umap"]
silhouette_scores = [0.3147, silhouette_hierarchical, silhouette_dbscan, silhouette_rfme, silhouette_gmm, silhouette_kproto, silhouette_scores[best_k-2] if best_k-2 < len(silhouette_scores) else silhouette_scores[-1]]
sns.barplot(x=models, y=silhouette_scores, palette="viridis")
plt.title("Clustering Model Comparison (Silhouette Scores)")
plt.ylabel("Silhouette Score")
plt.xticks(rotation=45)
plt.show()
/var/folders/4g/7cwxt52n09sb7vzyh6tzj3w00000gn/T/ipykernel_80884/2616664016.py:5: FutureWarning: Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect. sns.barplot(x=models, y=silhouette_scores, palette="viridis")
In [132]:
# Cluster personas
print("\n--- CLUSTER PERSONAS ---")
print("K-Means:")
print(cluster_profiles_kmeans)
print("\nHierarchical:")
print(cluster_profiles_hierarchical)
print("\nDBSCAN:")
print(dbscan_profiles)
print("\nRFME:")
print(cluster_summary_rfme)
print("\nGMM:")
print(gmm_summary)
print("\nK-Prototypes:")
print(cluster_summary_kproto)
print("\nUmap:")
print(segment_stats)
--- CLUSTER PERSONAS ---
K-Means:
KMeans_Cluster Recency Frequency Monetary Loyalty_Score \
0 0 25.771800 52.000000 58.972171 77.771800
1 1 13.091873 11.617197 38.994111 24.709069
2 2 37.673243 11.458693 38.987670 49.131936
3 3 38.209467 11.750296 80.422485 49.959763
4 4 12.879673 11.601636 80.155374 24.481308
Engagement_Score Persona
0 3.759555 High-Value Loyal Customer (High monetary spend...
1 3.713310 Recent Engaged Shopper (Good engagement score ...
2 3.711837 Frequent Low-Spender (High frequency but low m...
3 3.782485 Infrequent Low-Spender (Low frequency and low ...
4 3.784229 Dormant High-Value Customer (High recency, mea...
Hierarchical:
Hierarchical_Cluster Recency Frequency Monetary \
0 0 22.444444 11.807977 41.274644
1 1 25.771800 52.000000 58.972171
2 2 40.286783 12.375312 74.965087
3 3 16.517413 10.407960 85.492537
Persona
0 Premium Shopper (High spending and good freque...
1 Occasional Buyer (They spend less per transact...
2 At-Risk Customer (Low engagement, low monetary...
3 High-Frequency Budget Shopper(Very high freque...
DBSCAN:
DBSCAN_Cluster Recency Frequency Monetary \
0 0 25.028466 24.995409 59.877870
1 1 25.771800 52.000000 58.972171
2 2 25.406690 5.191901 59.897887
Persona
0 Premium Loyalist (High monetary value, moderat...
1 High-Frequency Deal Seeker (This group buys ve...
2 Low-Value Irregular Shopper (They have low spe...
RFME:
RFME_Cluster Recency Frequency Monetary Engagement_Score \
0 0 37.868243 11.608108 79.815878 4.329392
1 1 11.694405 11.319943 61.969871 4.427260
2 2 25.771800 52.000000 58.972171 3.759555
3 3 13.551020 11.457483 38.957483 3.222449
4 4 38.305292 11.502035 37.633650 3.716147
5 5 24.380187 12.101740 80.599732 3.100402
Persona
0 High-Value Loyal Customer (High monetary spend...
1 Recent Engaged Shopper (High engagement score ...
2 Frequent Low-Spender (Extremely high frequency...
3 Infrequent Low-Spender (Low monetary and moder...
4 Dormant High-Value Customer (High recency (mea...
5 High-Frequency Premium Buyer (High frequency a...
GMM:
GMM_Cluster Recency Frequency Monetary Engagement_Score \
0 0 25.500556 8.129032 81.880979 4.314127
1 1 30.163636 15.282828 57.055556 3.732727
2 2 39.261792 38.353774 67.943396 4.166038
3 3 12.737978 8.368008 50.680079 3.375859
4 4 28.973592 34.889085 39.672535 3.247535
Persona
0 Low Engagement Browser (Low across all metrics...
1 Moderate Value Loyal (Medium recency, frequenc...
2 High-Value Premium (High monetary value with g...
3 Recent Low-Value Explorer (Low recency (recent...
4 Occasional Big Spender (High monetary value bu...
K-Prototypes:
KPrototypes_Cluster Age Frequency Monetary Engagement_Score \
0 0 30.327064 11.933124 77.545455 3.765935
1 1 44.228246 11.661981 37.019411 3.715730
2 2 57.913187 11.179121 78.875824 3.783626
3 3 44.649351 52.000000 58.972171 3.759555
Recency Persona
0 24.938349 Luxury High-Spender (High frequency and modera...
1 25.068942 Infrequent High-Spender (Older age with modera...
2 26.001099 Loyal Subscription Shopper (Moderate frequency...
3 25.771800 Moderate-Frequency Engaged Shopper (Lower rece...
Umap:
Count Purchase Amount (USD) Age Previous Purchases \
Cluster
0 405 39.65 55.91 35.93
1 429 78.36 32.95 13.55
2 434 41.45 56.64 15.98
3 272 58.74 41.52 14.04
4 457 40.60 30.77 14.32
5 432 44.10 33.79 37.31
6 266 59.07 47.77 37.85
7 383 81.11 57.52 38.05
8 396 78.86 31.09 36.35
9 426 79.40 55.19 14.20
Review Rating Percentage
Cluster
0 4.29 10.4
1 3.16 11.0
2 3.16 11.1
3 3.61 7.0
4 4.26 11.7
5 3.13 11.1
6 3.90 6.8
7 3.37 9.8
8 4.33 10.2
9 4.31 10.9
In [135]:
#Cluster size comparison across models
plt.figure(figsize=(12, 6))
models = ["K-Means", "Hierarchical", "DBSCAN", "RFME", "GMM", "K-Prototypes", "Umap"]
cluster_counts = [len(cluster_profiles_kmeans), len(cluster_profiles_hierarchical), len(dbscan_profiles), len(cluster_summary_rfme), len(gmm_summary), len(cluster_summary_kproto), len(segment_stats)]
sns.barplot(x=models, y=cluster_counts, palette="viridis")
plt.title("Cluster Size Comparison Across Models")
plt.ylabel("Number of Clusters")
plt.xticks(rotation=45)
plt.show()
/var/folders/4g/7cwxt52n09sb7vzyh6tzj3w00000gn/T/ipykernel_80884/1550642355.py:5: FutureWarning: Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect. sns.barplot(x=models, y=cluster_counts, palette="viridis")
In [165]:
# Compute mean values for each method's cluster, excluding non-numeric columns
cluster_means_combined = pd.concat([
cluster_profiles_kmeans.select_dtypes(include=[np.number]),
cluster_profiles_hierarchical.select_dtypes(include=[np.number]),
dbscan_profiles.select_dtypes(include=[np.number]),
cluster_summary_rfme.select_dtypes(include=[np.number]),
gmm_summary.select_dtypes(include=[np.number]),
cluster_summary_kproto.select_dtypes(include=[np.number]),
segment_stats.select_dtypes(include=[np.number])
])
# Heatmap of cluster means
plt.figure(figsize=(14, 10))
sns.heatmap(cluster_means_combined, annot=True, cmap="viridis")
plt.title("Cluster Means Comparison")
plt.show()
Section 12: Best Model GMM¶
In [153]:
gmm_profiles = df_gmm.groupby("GMM_Cluster")[features_gmm].mean().reset_index()
print("\nGMM Cluster Profiles:")
print(gmm_profiles)
GMM Cluster Profiles: GMM_Cluster Recency Frequency Monetary Engagement_Score 0 0 25.500556 8.129032 81.880979 4.314127 1 1 30.163636 15.282828 57.055556 3.732727 2 2 39.261792 38.353774 67.943396 4.166038 3 3 12.737978 8.368008 50.680079 3.375859 4 4 28.973592 34.889085 39.672535 3.247535
In [154]:
#Heatmap GMM cluster profiles
plt.figure(figsize=(12, 6))
sns.heatmap(gmm_profiles, annot=True, cmap="coolwarm")
plt.title("GMM Cluster Profiles")
plt.show()
In [159]:
# Plot of numerical features to check in GMM
features_gmm = ['Recency', 'Frequency', 'Monetary', 'Engagement_Score']
plt.figure(figsize=(12, 6))
sns.pairplot(df_gmm, hue="GMM_Cluster", vars=features_gmm, palette="viridis")
plt.suptitle("GMM Clustering - Pairplot of Numerical Features")
plt.show()
# Boxplot of spending by cluster in GMM
plt.figure(figsize=(12, 6))
sns.boxplot(x="GMM_Cluster", y="Monetary", data=df_gmm, palette="viridis")
plt.title("Monetary Spend Distribution Across Clusters (GMM)")
plt.show()
# Boxplot of age by cluster in GMM
plt.figure(figsize=(12, 6))
sns.boxplot(x="GMM_Cluster", y="Age", data=df_gmm, palette="viridis")
plt.title("Age Distribution Across Clusters (GMM)")
plt.show()
# Boxplot of engagement score by cluster in GMM
plt.figure(figsize=(12, 6))
sns.boxplot(x="GMM_Cluster", y="Engagement_Score", data=df_gmm, palette="viridis")
plt.title("Engagement Score Distribution Across Clusters (GMM)")
plt.show()
# Boxplot of recency by cluster in GMM
plt.figure(figsize=(12, 6))
sns.boxplot(x="GMM_Cluster", y="Recency", data=df_gmm, palette="viridis")
plt.title("Recency Distribution Across Clusters (GMM)")
plt.show()
# Boxplot of frequency by cluster in GMM
plt.figure(figsize=(12, 6))
sns.boxplot(x="GMM_Cluster", y="Frequency", data=df_gmm, palette="viridis")
plt.title("Frequency Distribution Across Clusters (GMM)")
plt.show()
<Figure size 1200x600 with 0 Axes>
/var/folders/4g/7cwxt52n09sb7vzyh6tzj3w00000gn/T/ipykernel_80884/2081717367.py:11: FutureWarning: Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect. sns.boxplot(x="GMM_Cluster", y="Monetary", data=df_gmm, palette="viridis")
/var/folders/4g/7cwxt52n09sb7vzyh6tzj3w00000gn/T/ipykernel_80884/2081717367.py:17: FutureWarning: Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect. sns.boxplot(x="GMM_Cluster", y="Age", data=df_gmm, palette="viridis")
/var/folders/4g/7cwxt52n09sb7vzyh6tzj3w00000gn/T/ipykernel_80884/2081717367.py:23: FutureWarning: Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect. sns.boxplot(x="GMM_Cluster", y="Engagement_Score", data=df_gmm, palette="viridis")
/var/folders/4g/7cwxt52n09sb7vzyh6tzj3w00000gn/T/ipykernel_80884/2081717367.py:29: FutureWarning: Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect. sns.boxplot(x="GMM_Cluster", y="Recency", data=df_gmm, palette="viridis")
/var/folders/4g/7cwxt52n09sb7vzyh6tzj3w00000gn/T/ipykernel_80884/2081717367.py:35: FutureWarning: Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect. sns.boxplot(x="GMM_Cluster", y="Frequency", data=df_gmm, palette="viridis")
In [168]:
# GMM personas
gmm_personas = {
0: "Low Engagement Browser (Low across all metrics, particularly engagement score. These customers browse occasionally but rarely convert to significant purchases.)",
1: "Moderate Value Loyal (Medium recency, frequency, and monetary values with good engagement. These are solid middle-tier customers.)",
2: "High-Value Premium (High monetary value with good recency and frequency. These are premium customers who make regular valuable purchases.)",
3: "Recent Low-Value Explorer (Low recency (recent purchases) but low monetary value. These may be new customers exploring the brand with small initial purchases.)",
4: "Occasional Big Spender (High monetary value but low frequency. These customers shop infrequently but spend significantly when they do.)"
}
df_gmm["GMM_Persona"] = df_gmm["GMM_Cluster"].map(gmm_personas)
# Cluster profiles with persona descriptions
gmm_profiles = df_gmm.groupby("GMM_Cluster")[features_gmm].mean().reset_index()
gmm_profiles["Persona"] = gmm_profiles["GMM_Cluster"].map(gmm_personas)
print("\nGMM Customer Personas:")
print(gmm_profiles)
GMM Customer Personas:
GMM_Cluster Recency Frequency Monetary Engagement_Score \
0 0 25.500556 8.129032 81.880979 4.314127
1 1 30.163636 15.282828 57.055556 3.732727
2 2 39.261792 38.353774 67.943396 4.166038
3 3 12.737978 8.368008 50.680079 3.375859
4 4 28.973592 34.889085 39.672535 3.247535
Persona
0 Low Engagement Browser (Low across all metrics...
1 Moderate Value Loyal (Medium recency, frequenc...
2 High-Value Premium (High monetary value with g...
3 Recent Low-Value Explorer (Low recency (recent...
4 Occasional Big Spender (High monetary value bu...
In [170]:
# Analyze the GMM cluster distribution
gmm_cluster_counts = df_gmm["GMM_Cluster"].value_counts()
print("\nGMM Cluster Distribution:")
print(gmm_cluster_counts)
GMM Cluster Distribution: GMM_Cluster 3 1019 1 990 0 899 4 568 2 424 Name: count, dtype: int64
Customer Segment Translations¶
Cluster 0: Premium High-Margin Shoppers¶
Average spend: $82 per transaction (82% of maximum possible spend)
Purchase pattern: Buy expensive items approximately once every 25-26 days
Engagement: Exceptionally high (4.3/5.0) - actively interacting with brand touchpoints
Business value: Likely responsible for 30-40% of total profit despite lower purchase frequency
Real-world example: Luxury shoppers who carefully select high-end items rather than making frequent purchases
Cluster 1: Steady Mid-Tier Customers¶
Average spend: $57 per transaction
Purchase pattern: Shop approximately every 2-3 weeks (15 times per period)
Recency: Last purchased about 30 days ago
Engagement: Good but not exceptional (3.7/5.0)
Real-world example: Regular customers who reliably purchase mid-priced items on a predictable schedule
Cluster 2: Enthusiast Power Users¶
Average spend: $68 per transaction
Purchase pattern: Most frequent shoppers at 38 purchases (approaching weekly visits)
Recency: Longest time since last purchase (39 days) - potential risk of churn
Engagement: Very high (4.2/5.0) - brand enthusiasts
Real-world example: Passionate hobbyists or product category enthusiasts who frequently buy related items
Cluster 3: Recent New/Reactivated Customers¶
Average spend: $51 per transaction
Purchase pattern: Infrequent (8-9 purchases total)
Recency: Most recent shoppers (purchased within the last 13 days)
Engagement: Moderate (3.4/5.0)
Real-world example: Newly acquired customers or recently reactivated dormant customers who haven't established a regular pattern yet
Cluster 4: Budget-Conscious Frequent Shoppers¶
Average spend: $40 per transaction (lowest spending group)
Purchase pattern: Very frequent (35 purchases) - about every 10 days
Recency: Moderately recent (29 days since last purchase)
Engagement: Lowest of all groups (3.2/5.0)
Real-world example: Price-sensitive frequent shoppers who make smaller purchases, likely deal-hunters or necessity-based shoppers
In [172]:
# Calculate key metrics
avg_purchase = segment_data['Purchase Amount (USD)'].mean()
freq_counts = segment_data['Frequency of Purchases'].value_counts().nlargest(2)
avg_previous = segment_data['Previous Purchases'].mean()
avg_rating = segment_data['Review Rating'].mean()
discount_usage = (segment_data['Discount Applied'] == 'Yes').mean() * 100
promo_usage = (segment_data['Promo Code Used'] == 'Yes').mean() * 100
subscription_rate = (segment_data['Subscription Status'] == 'Yes').mean() * 100
In [173]:
# Top categories and products
top_categories = segment_data['Category'].value_counts().nlargest(2)
top_items = segment_data['Item Purchased'].value_counts().nlargest(3)
top_colors = segment_data['Color'].value_counts().nlargest(2)
In [181]:
# Segment profile
print("\nKEY CHARACTERISTICS:")
print(f"• Average purchase: ${avg_purchase:.2f}")
print(f"• Purchase frequency: {', '.join([f'{freq} ({count})' for freq, count in freq_counts.items()])}")
print(f"• Previous purchases: {avg_previous:.1f}")
print(f"• Average rating: {avg_rating:.1f}/5")
print(f"• Discount usage: {discount_usage:.1f}%")
print(f"• Promo code usage: {promo_usage:.1f}%")
print(f"• Subscription rate: {subscription_rate:.1f}%")
print("\nPRODUCT PREFERENCES:")
print("• Top categories:")
for cat, count in top_categories.items():
print(f" - {cat}: {count} purchases ({count/len(segment_data)*100:.1f}% of segment)")
print("• Top products:")
for item, count in top_items.items():
print(f" - {item}: {count} purchases")
print("• Top colors:")
for color, count in top_colors.items():
print(f" - {color}: {count} purchases")
#Segment name them by cluster number
segment_names = {
0: "High-Value Loyal Customer",
1: "Recent Engaged Shopper",
2: "Frequent Low-Spender",
3: "Infrequent Low-Spender",
4: "Dormant High-Value Customer",
5: "High-Frequency Premium Buyer"
}
KEY CHARACTERISTICS: • Average purchase: $79.40 • Purchase frequency: Annually (79), Monthly (78) • Previous purchases: 14.2 • Average rating: 4.3/5 • Discount usage: 44.1% • Promo code usage: 44.1% • Subscription rate: 0.0% PRODUCT PREFERENCES: • Top categories: - Clothing: 186 purchases (43.7% of segment) - Accessories: 127 purchases (29.8% of segment) • Top products: - Jacket: 31 purchases - T-shirt: 25 purchases - Dress: 23 purchases • Top colors: - Yellow: 26 purchases - Green: 24 purchases - Orange: 23 purchases
In [182]:
# Generate segment insights and marketing recommendations
print("\n--- SEGMENT PROFILES & MARKETING RECOMMENDATIONS ---")
for cluster, segment_name in segment_names.items():
segment_data = df[df['Cluster'] == cluster]
count = len(segment_data)
pct = count / len(df) * 100
print(f"\n=== {segment_name} ===")
print(f"Size: {count} customers ({pct:.1f}% of total customers)")
# Calculate key metrics
avg_purchase = segment_data['Purchase Amount (USD)'].mean()
freq_counts = segment_data['Frequency of Purchases'].value_counts().nlargest(2)
avg_previous = segment_data['Previous Purchases'].mean()
avg_rating = segment_data['Review Rating'].mean()
discount_usage = (segment_data['Discount Applied'] == 'Yes').mean() * 100
promo_usage = (segment_data['Promo Code Used'] == 'Yes').mean() * 100
subscription_rate = (segment_data['Subscription Status'] == 'Yes').mean() * 100
# Top categories and products
top_categories = segment_data['Category'].value_counts().nlargest(2)
top_items = segment_data['Item Purchased'].value_counts().nlargest(3)
top_seasons = segment_data['Season'].value_counts().nlargest(2)
top_colors = segment_data['Color'].value_counts().nlargest(3)
# Print segment profile
print("\nKEY CHARACTERISTICS:")
print(f"• Average purchase: ${avg_purchase:.2f}")
print(f"• Purchase frequency: {', '.join([f'{freq} ({count})' for freq, count in freq_counts.items()])}")
print(f"• Previous purchases: {avg_previous:.1f}")
print(f"• Average rating: {avg_rating:.1f}/5")
print(f"• Discount usage: {discount_usage:.1f}%")
print(f"• Promo code usage: {promo_usage:.1f}%")
print(f"• Subscription rate: {subscription_rate:.1f}%")
print("\nPRODUCT PREFERENCES:")
print("• Top categories:")
for cat, count in top_categories.items():
print(f" - {cat}: {count} purchases ({count/len(segment_data)*100:.1f}% of segment)")
print("• Top items:")
for item, count in top_items.items():
print(f" - {item}: {count} purchases")
print("• Top seasons:")
for season, count in top_seasons.items():
print(f" - {season}: {count} purchases")
print("• Top colors:")
for color, count in top_colors.items():
print(f" - {color}: {count} purchases")
--- SEGMENT PROFILES & MARKETING RECOMMENDATIONS --- === High-Value Loyal Customer === Size: 405 customers (10.4% of total customers) KEY CHARACTERISTICS: • Average purchase: $39.65 • Purchase frequency: Fortnightly (75), Every 3 Months (72) • Previous purchases: 35.9 • Average rating: 4.3/5 • Discount usage: 43.2% • Promo code usage: 43.2% • Subscription rate: 0.0% PRODUCT PREFERENCES: • Top categories: - Clothing: 169 purchases (41.7% of segment) - Accessories: 137 purchases (33.8% of segment) • Top items: - Jewelry: 31 purchases - Blouse: 26 purchases - Gloves: 24 purchases • Top seasons: - Spring: 106 purchases - Summer: 102 purchases • Top colors: - Silver: 23 purchases - Turquoise: 23 purchases - Teal: 23 purchases === Recent Engaged Shopper === Size: 429 customers (11.0% of total customers) KEY CHARACTERISTICS: • Average purchase: $78.36 • Purchase frequency: Bi-Weekly (83), Every 3 Months (76) • Previous purchases: 13.5 • Average rating: 3.2/5 • Discount usage: 41.7% • Promo code usage: 41.7% • Subscription rate: 0.0% PRODUCT PREFERENCES: • Top categories: - Clothing: 185 purchases (43.1% of segment) - Accessories: 146 purchases (34.0% of segment) • Top items: - Backpack: 23 purchases - Sunglasses: 23 purchases - Shirt: 22 purchases • Top seasons: - Fall: 119 purchases - Winter: 109 purchases • Top colors: - Green: 25 purchases - Teal: 22 purchases - Maroon: 21 purchases === Frequent Low-Spender === Size: 434 customers (11.1% of total customers) KEY CHARACTERISTICS: • Average purchase: $41.45 • Purchase frequency: Annually (88), Quarterly (79) • Previous purchases: 16.0 • Average rating: 3.2/5 • Discount usage: 42.9% • Promo code usage: 42.9% • Subscription rate: 0.0% PRODUCT PREFERENCES: • Top categories: - Clothing: 193 purchases (44.5% of segment) - Accessories: 125 purchases (28.8% of segment) • Top items: - Shoes: 24 purchases - Blouse: 23 purchases - Sandals: 23 purchases • Top seasons: - Spring: 119 purchases - Summer: 111 purchases • Top colors: - Blue: 24 purchases - Beige: 24 purchases - Teal: 22 purchases === Infrequent Low-Spender === Size: 272 customers (7.0% of total customers) KEY CHARACTERISTICS: • Average purchase: $58.74 • Purchase frequency: Weekly (272) • Previous purchases: 14.0 • Average rating: 3.6/5 • Discount usage: 39.3% • Promo code usage: 39.3% • Subscription rate: 0.0% PRODUCT PREFERENCES: • Top categories: - Clothing: 131 purchases (48.2% of segment) - Accessories: 77 purchases (28.3% of segment) • Top items: - Shorts: 18 purchases - Sweater: 16 purchases - Pants: 14 purchases • Top seasons: - Spring: 73 purchases - Fall: 69 purchases • Top colors: - Cyan: 18 purchases - Yellow: 16 purchases - Lavender: 14 purchases === Dormant High-Value Customer === Size: 457 customers (11.7% of total customers) KEY CHARACTERISTICS: • Average purchase: $40.60 • Purchase frequency: Every 3 Months (88), Fortnightly (81) • Previous purchases: 14.3 • Average rating: 4.3/5 • Discount usage: 40.7% • Promo code usage: 40.7% • Subscription rate: 0.0% PRODUCT PREFERENCES: • Top categories: - Clothing: 196 purchases (42.9% of segment) - Accessories: 159 purchases (34.8% of segment) • Top items: - Belt: 30 purchases - Socks: 25 purchases - Pants: 23 purchases • Top seasons: - Spring: 126 purchases - Summer: 122 purchases • Top colors: - Olive: 34 purchases - Magenta: 26 purchases - Black: 25 purchases === High-Frequency Premium Buyer === Size: 432 customers (11.1% of total customers) KEY CHARACTERISTICS: • Average purchase: $44.10 • Purchase frequency: Quarterly (78), Monthly (76) • Previous purchases: 37.3 • Average rating: 3.1/5 • Discount usage: 49.8% • Promo code usage: 49.8% • Subscription rate: 0.0% PRODUCT PREFERENCES: • Top categories: - Clothing: 199 purchases (46.1% of segment) - Accessories: 139 purchases (32.2% of segment) • Top items: - Shorts: 25 purchases - Pants: 24 purchases - Scarf: 24 purchases • Top seasons: - Winter: 123 purchases - Summer: 113 purchases • Top colors: - Green: 25 purchases - Peach: 21 purchases - Blue: 20 purchases
In [192]:
# Cluster 0 key metrics from GMM
plt.figure(figsize=(12, 6))
segment_data = df_gmm[df_gmm["GMM_Cluster"] == 0]
segment_data_mean = segment_data[features_gmm].mean()
colors = ["skyblue", "lightgreen", "salmon", "gold"]
segment_data_mean.plot(kind="bar", color=colors)
plt.title("GMM Cluster 0 Key Metrics")
plt.ylabel("Mean Value")
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()
# Cluster 1
plt.figure(figsize=(12, 6))
segment_data = df_gmm[df_gmm["GMM_Cluster"] == 1]
segment_data_mean = segment_data[features_gmm].mean()
colors = ["skyblue", "lightgreen", "salmon", "gold"]
segment_data_mean.plot(kind="bar", color=colors)
plt.title("GMM Cluster 1 Key Metrics")
plt.ylabel("Mean Value")
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()
# Cluster 2
plt.figure(figsize=(12, 6))
segment_data = df_gmm[df_gmm["GMM_Cluster"] == 2]
segment_data_mean = segment_data[features_gmm].mean()
colors = ["skyblue", "lightgreen", "salmon", "gold"]
segment_data_mean.plot(kind="bar", color=colors)
plt.title("GMM Cluster 2 Key Metrics")
plt.ylabel("Mean Value")
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()
# Cluster 3
plt.figure(figsize=(12, 6))
segment_data = df_gmm[df_gmm["GMM_Cluster"] == 3]
segment_data_mean = segment_data[features_gmm].mean()
colors = ["skyblue", "lightgreen", "salmon", "gold"]
segment_data_mean.plot(kind="bar", color=colors)
plt.title("GMM Cluster 3 Key Metrics")
plt.ylabel("Mean Value")
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()
# Cluster 4
plt.figure(figsize=(12, 6))
segment_data = df_gmm[df_gmm["GMM_Cluster"] == 4]
segment_data_mean = segment_data[features_gmm].mean()
colors = ["skyblue", "lightgreen", "salmon", "gold"]
segment_data_mean.plot(kind="bar", color=colors)
plt.title("GMM Cluster 4 Key Metrics")
plt.ylabel("Mean Value")
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()
In [193]:
print("\n--- SEGMENT PROFILES & MARKETING RECOMMENDATIONS BASED ON GMM ---")
segment_descriptions = {
"Low Engagement Browser": "Customers who browse occasionally but rarely convert to significant purchases.",
"Moderate Value Loyal": "Customers with medium recency, frequency, and monetary values, showing good engagement. Solid middle-tier customers.",
"High-Value Premium": "Premium customers with high monetary value, regular purchases, and good recency/frequency metrics.",
"Recent Low-Value Explorer": "Newer customers who have made recent purchases but at a lower monetary value. They may be exploring the brand.",
"Occasional Big Spender": "Customers who shop infrequently but spend significantly when they do."
}
marketing_recommendations = {
"Low Engagement Browser": [
"Implement targeted email campaigns to re-engage these customers",
"Use retargeting ads to remind them of past views and interests",
"Offer exclusive first-time purchase discounts",
"Test interactive content such as quizzes to understand their interests",
"Incentivize small initial purchases with limited-time offers"
],
"Moderate Value Loyal": [
"Develop a rewards program with tiered benefits to enhance loyalty",
"Offer personalized recommendations based on past purchases",
"Provide special early-access to new product launches",
"Encourage referrals with discounts or perks",
"Send engagement-driven content such as styling tips, usage guides, etc."
],
"High-Value Premium": [
"Create an exclusive VIP membership program with premium perks",
"Provide white-glove customer service and personal shopping assistance",
"Offer invitation-only events and product pre-orders",
"Bundle high-end products with exclusive limited-time collections",
"Highlight brand values and storytelling in marketing materials"
],
"Recent Low-Value Explorer": [
"Implement a nurturing email series introducing product benefits",
"Encourage repeat purchases with 'next purchase' incentives",
"Use social proof (reviews, testimonials) to build trust",
"Showcase best-selling items in follow-up marketing campaigns",
"Offer personalized first-purchase discounts to increase retention"
],
"Occasional Big Spender": [
"Send personalized high-end product recommendations",
"Offer concierge-style services to enhance the shopping experience",
"Provide targeted promotions around peak spending periods",
"Introduce high-value loyalty perks for large purchase sizes",
"Encourage category expansion with curated bundles"
]
}
--- SEGMENT PROFILES & MARKETING RECOMMENDATIONS BASED ON GMM ---
In [194]:
product_strategies = {
"Low Engagement Browser": "Highlight entry-level products, bestsellers, and accessible price points.",
"Moderate Value Loyal": "Offer personalized product bundles and exclusive early access to promotions.",
"High-Value Premium": "Showcase luxury items, premium collections, and exclusive limited-edition products.",
"Recent Low-Value Explorer": "Promote starter sets, introductory offers, and educational product content.",
"Occasional Big Spender": "Feature high-ticket items, exclusive bundles, and premium experience-driven products."
}
# Updated function to handle unknown segments with GMM-based logic
def get_generic_description(segment_name):
if "Low Engagement" in segment_name or "Browser" in segment_name:
return segment_descriptions["Low Engagement Browser"]
elif "Moderate" in segment_name or "Loyal" in segment_name:
return segment_descriptions["Moderate Value Loyal"]
elif "High-Value" in segment_name or "Premium" in segment_name:
return segment_descriptions["High-Value Premium"]
elif "Recent" in segment_name or "Explorer" in segment_name:
return segment_descriptions["Recent Low-Value Explorer"]
elif "Occasional" in segment_name or "Big Spender" in segment_name:
return segment_descriptions["Occasional Big Spender"]
else:
return "Customers with specific shopping patterns and preferences."
def get_generic_recommendations(segment_name):
if "Low Engagement" in segment_name or "Browser" in segment_name:
return marketing_recommendations["Low Engagement Browser"]
elif "Moderate" in segment_name or "Loyal" in segment_name:
return marketing_recommendations["Moderate Value Loyal"]
elif "High-Value" in segment_name or "Premium" in segment_name:
return marketing_recommendations["High-Value Premium"]
elif "Recent" in segment_name or "Explorer" in segment_name:
return marketing_recommendations["Recent Low-Value Explorer"]
elif "Occasional" in segment_name or "Big Spender" in segment_name:
return marketing_recommendations["Occasional Big Spender"]
else:
return [
"Test various engagement strategies to refine segmentation",
"Develop targeted outreach campaigns to drive conversions",
"Analyze buying behavior for deeper insights into shopping patterns",
"Implement automated re-engagement emails and promotions",
"Create A/B tests for personalized product recommendations"
]
def get_generic_product_strategy(segment_name):
if "Low Engagement" in segment_name or "Browser" in segment_name:
return product_strategies["Low Engagement Browser"]
elif "Moderate" in segment_name or "Loyal" in segment_name:
return product_strategies["Moderate Value Loyal"]
elif "High-Value" in segment_name or "Premium" in segment_name:
return product_strategies["High-Value Premium"]
elif "Recent" in segment_name or "Explorer" in segment_name:
return product_strategies["Recent Low-Value Explorer"]
elif "Occasional" in segment_name or "Big Spender" in segment_name:
return product_strategies["Occasional Big Spender"]
else:
return "Refine product recommendations based on customer behavior and purchase history."
In [196]:
# Generate segment insights and marketing recommendations
print("\n--- SEGMENT PROFILES & MARKETING RECOMMENDATIONS ---")
for cluster, segment_name in segment_names.items():
segment_data = df[df['Cluster'] == cluster]
count = len(segment_data)
pct = count / len(df) * 100
print(f"\n=== {segment_name} ===")
print(f"Size: {count} customers ({pct:.1f}% of total customers)")
# Calculate key metrics
avg_purchase = segment_data['Purchase Amount (USD)'].mean()
freq_counts = segment_data['Frequency of Purchases'].value_counts().nlargest(2)
avg_previous = segment_data['Previous Purchases'].mean()
avg_rating = segment_data['Review Rating'].mean()
discount_usage = (segment_data['Discount Applied'] == 'Yes').mean() * 100
promo_usage = (segment_data['Promo Code Used'] == 'Yes').mean() * 100
subscription_rate = (segment_data['Subscription Status'] == 'Yes').mean() * 100
# Top categories and products
top_categories = segment_data['Category'].value_counts().nlargest(2)
top_items = segment_data['Item Purchased'].value_counts().nlargest(3)
top_seasons = segment_data['Season'].value_counts().nlargest(2)
top_colors = segment_data['Color'].value_counts().nlargest(3)
# Print segment profile
print("\nKEY CHARACTERISTICS:")
print(f"• Average purchase: ${avg_purchase:.2f}")
print(f"• Purchase frequency: {', '.join([f'{freq} ({count})' for freq, count in freq_counts.items()])}")
print(f"• Previous purchases: {avg_previous:.1f}")
print(f"• Average rating: {avg_rating:.1f}/5")
print(f"• Discount usage: {discount_usage:.1f}%")
print(f"• Promo code usage: {promo_usage:.1f}%")
print(f"• Subscription rate: {subscription_rate:.1f}%")
print("\nPRODUCT PREFERENCES:")
print("• Top categories:")
for cat, count in top_categories.items():
print(f" - {cat}: {count} purchases ({count/len(segment_data)*100:.1f}% of segment)")
print("• Top items:")
for item, count in top_items.items():
print(f" - {item}: {
count} purchases")
print("• Top seasons:")
for season, count in top_seasons.items():
print(f" - {season}: {count} purchases")
print("• Top colors:")
for color, count in top_colors.items():
print(f" - {color}: {count} purchases")
# Segment-specific recommendations
print("\nMARKETING RECOMMENDATIONS:")
print(get_generic_description(segment_name))
print(get_generic_recommendations(segment_name))
print("\nPRODUCT STRATEGY:")
print(get_generic_product_strategy(segment_name))
--- SEGMENT PROFILES & MARKETING RECOMMENDATIONS --- === High-Value Loyal Customer === Size: 405 customers (10.4% of total customers) KEY CHARACTERISTICS: • Average purchase: $39.65 • Purchase frequency: Fortnightly (75), Every 3 Months (72) • Previous purchases: 35.9 • Average rating: 4.3/5 • Discount usage: 43.2% • Promo code usage: 43.2% • Subscription rate: 0.0% PRODUCT PREFERENCES: • Top categories: - Clothing: 169 purchases (41.7% of segment) - Accessories: 137 purchases (33.8% of segment) • Top items: - Jewelry: 31 purchases - Blouse: 26 purchases - Gloves: 24 purchases • Top seasons: - Spring: 106 purchases - Summer: 102 purchases • Top colors: - Silver: 23 purchases - Turquoise: 23 purchases - Teal: 23 purchases MARKETING RECOMMENDATIONS: Customers with medium recency, frequency, and monetary values, showing good engagement. Solid middle-tier customers. ['Develop a rewards program with tiered benefits to enhance loyalty', 'Offer personalized recommendations based on past purchases', 'Provide special early-access to new product launches', 'Encourage referrals with discounts or perks', 'Send engagement-driven content such as styling tips, usage guides, etc.'] PRODUCT STRATEGY: Offer personalized product bundles and exclusive early access to promotions. === Recent Engaged Shopper === Size: 429 customers (11.0% of total customers) KEY CHARACTERISTICS: • Average purchase: $78.36 • Purchase frequency: Bi-Weekly (83), Every 3 Months (76) • Previous purchases: 13.5 • Average rating: 3.2/5 • Discount usage: 41.7% • Promo code usage: 41.7% • Subscription rate: 0.0% PRODUCT PREFERENCES: • Top categories: - Clothing: 185 purchases (43.1% of segment) - Accessories: 146 purchases (34.0% of segment) • Top items: - Backpack: 23 purchases - Sunglasses: 23 purchases - Shirt: 22 purchases • Top seasons: - Fall: 119 purchases - Winter: 109 purchases • Top colors: - Green: 25 purchases - Teal: 22 purchases - Maroon: 21 purchases MARKETING RECOMMENDATIONS: Newer customers who have made recent purchases but at a lower monetary value. They may be exploring the brand. ['Implement a nurturing email series introducing product benefits', "Encourage repeat purchases with 'next purchase' incentives", 'Use social proof (reviews, testimonials) to build trust', 'Showcase best-selling items in follow-up marketing campaigns', 'Offer personalized first-purchase discounts to increase retention'] PRODUCT STRATEGY: Promote starter sets, introductory offers, and educational product content. === Frequent Low-Spender === Size: 434 customers (11.1% of total customers) KEY CHARACTERISTICS: • Average purchase: $41.45 • Purchase frequency: Annually (88), Quarterly (79) • Previous purchases: 16.0 • Average rating: 3.2/5 • Discount usage: 42.9% • Promo code usage: 42.9% • Subscription rate: 0.0% PRODUCT PREFERENCES: • Top categories: - Clothing: 193 purchases (44.5% of segment) - Accessories: 125 purchases (28.8% of segment) • Top items: - Shoes: 24 purchases - Blouse: 23 purchases - Sandals: 23 purchases • Top seasons: - Spring: 119 purchases - Summer: 111 purchases • Top colors: - Blue: 24 purchases - Beige: 24 purchases - Teal: 22 purchases MARKETING RECOMMENDATIONS: Customers with specific shopping patterns and preferences. ['Test various engagement strategies to refine segmentation', 'Develop targeted outreach campaigns to drive conversions', 'Analyze buying behavior for deeper insights into shopping patterns', 'Implement automated re-engagement emails and promotions', 'Create A/B tests for personalized product recommendations'] PRODUCT STRATEGY: Refine product recommendations based on customer behavior and purchase history. === Infrequent Low-Spender === Size: 272 customers (7.0% of total customers) KEY CHARACTERISTICS: • Average purchase: $58.74 • Purchase frequency: Weekly (272) • Previous purchases: 14.0 • Average rating: 3.6/5 • Discount usage: 39.3% • Promo code usage: 39.3% • Subscription rate: 0.0% PRODUCT PREFERENCES: • Top categories: - Clothing: 131 purchases (48.2% of segment) - Accessories: 77 purchases (28.3% of segment) • Top items: - Shorts: 18 purchases - Sweater: 16 purchases - Pants: 14 purchases • Top seasons: - Spring: 73 purchases - Fall: 69 purchases • Top colors: - Cyan: 18 purchases - Yellow: 16 purchases - Lavender: 14 purchases MARKETING RECOMMENDATIONS: Customers with specific shopping patterns and preferences. ['Test various engagement strategies to refine segmentation', 'Develop targeted outreach campaigns to drive conversions', 'Analyze buying behavior for deeper insights into shopping patterns', 'Implement automated re-engagement emails and promotions', 'Create A/B tests for personalized product recommendations'] PRODUCT STRATEGY: Refine product recommendations based on customer behavior and purchase history. === Dormant High-Value Customer === Size: 457 customers (11.7% of total customers) KEY CHARACTERISTICS: • Average purchase: $40.60 • Purchase frequency: Every 3 Months (88), Fortnightly (81) • Previous purchases: 14.3 • Average rating: 4.3/5 • Discount usage: 40.7% • Promo code usage: 40.7% • Subscription rate: 0.0% PRODUCT PREFERENCES: • Top categories: - Clothing: 196 purchases (42.9% of segment) - Accessories: 159 purchases (34.8% of segment) • Top items: - Belt: 30 purchases - Socks: 25 purchases - Pants: 23 purchases • Top seasons: - Spring: 126 purchases - Summer: 122 purchases • Top colors: - Olive: 34 purchases - Magenta: 26 purchases - Black: 25 purchases MARKETING RECOMMENDATIONS: Premium customers with high monetary value, regular purchases, and good recency/frequency metrics. ['Create an exclusive VIP membership program with premium perks', 'Provide white-glove customer service and personal shopping assistance', 'Offer invitation-only events and product pre-orders', 'Bundle high-end products with exclusive limited-time collections', 'Highlight brand values and storytelling in marketing materials'] PRODUCT STRATEGY: Showcase luxury items, premium collections, and exclusive limited-edition products. === High-Frequency Premium Buyer === Size: 432 customers (11.1% of total customers) KEY CHARACTERISTICS: • Average purchase: $44.10 • Purchase frequency: Quarterly (78), Monthly (76) • Previous purchases: 37.3 • Average rating: 3.1/5 • Discount usage: 49.8% • Promo code usage: 49.8% • Subscription rate: 0.0% PRODUCT PREFERENCES: • Top categories: - Clothing: 199 purchases (46.1% of segment) - Accessories: 139 purchases (32.2% of segment) • Top items: - Shorts: 25 purchases - Pants: 24 purchases - Scarf: 24 purchases • Top seasons: - Winter: 123 purchases - Summer: 113 purchases • Top colors: - Green: 25 purchases - Peach: 21 purchases - Blue: 20 purchases MARKETING RECOMMENDATIONS: Premium customers with high monetary value, regular purchases, and good recency/frequency metrics. ['Create an exclusive VIP membership program with premium perks', 'Provide white-glove customer service and personal shopping assistance', 'Offer invitation-only events and product pre-orders', 'Bundle high-end products with exclusive limited-time collections', 'Highlight brand values and storytelling in marketing materials'] PRODUCT STRATEGY: Showcase luxury items, premium collections, and exclusive limited-edition products.
In [198]:
# Add UMAP1 and UMAP2 columns to df_gmm
df_gmm['UMAP1'] = viz_embedding[:, 0]
df_gmm['UMAP2'] = viz_embedding[:, 1]
# Visualize the GMM clusters
plt.figure(figsize=(14, 10))
sns.scatterplot(
data=df_gmm,
x='UMAP1',
y='UMAP2',
hue='GMM_Cluster',
palette='viridis',
s=100,
alpha=0.7
)
plt.title('GMM-Based Customer Segments', fontsize=16)
plt.legend(title='Segment', bbox_to_anchor=(1.05, 1), loc='upper left')
plt.tight_layout()
plt.show()
